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    "# Chapter 4\n",
    "`Original content created by Cam Davidson-Pilon`\n",
    "\n",
    "`Ported to Python 3 and PyMC3 by Max Margenot (@clean_utensils) and Thomas Wiecki (@twiecki) at Quantopian (@quantopian)`\n",
    "\n",
    "______\n",
    "\n",
    "## The greatest theorem never told\n",
    "\n",
    "\n",
    "This chapter focuses on an idea that is always bouncing around our minds, but is rarely made explicit outside books devoted to statistics. In fact, we've been using this simple idea in every example thus far. "
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    "### The Law of Large Numbers\n",
    "\n",
    "Let $Z_i$ be $N$ independent samples from some probability distribution. According to *the Law of Large numbers*,  so long as the expected value $E[Z]$ is finite, the following holds,\n",
    "\n",
    "$$\\frac{1}{N} \\sum_{i=1}^N Z_i \\rightarrow E[ Z ],  \\;\\;\\; N \\rightarrow \\infty.$$\n",
    "\n",
    "In words:\n",
    "\n",
    ">   The average of a sequence of random variables from the same distribution converges to the expected value of that distribution.\n",
    "\n",
    "This may seem like a boring result, but it will be the most useful tool you use."
   ]
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    "### Intuition \n",
    "\n",
    "If the above Law is somewhat surprising,  it can be made more clear by examining a simple example. \n",
    "\n",
    "Consider a random variable $Z$ that can take only two values, $c_1$ and $c_2$. Suppose we have a large number of samples of $Z$, denoting a specific sample $Z_i$. The Law says that we can approximate the expected value of $Z$ by averaging over all samples. Consider the average:\n",
    "\n",
    "\n",
    "$$ \\frac{1}{N} \\sum_{i=1}^N \\;Z_i $$\n",
    "\n",
    "\n",
    "By construction, $Z_i$ can only take on $c_1$ or $c_2$, hence we can partition the sum over these two values:\n",
    "\n",
    "\\begin{align}\n",
    "\\frac{1}{N} \\sum_{i=1}^N \\;Z_i\n",
    "& =\\frac{1}{N} \\big(  \\sum_{ Z_i = c_1}c_1 + \\sum_{Z_i=c_2}c_2 \\big) \\\\\\\\[5pt]\n",
    "& = c_1 \\sum_{ Z_i = c_1}\\frac{1}{N} + c_2 \\sum_{ Z_i = c_2}\\frac{1}{N} \\\\\\\\[5pt]\n",
    "& = c_1 \\times \\text{ (approximate frequency of $c_1$) } \\\\\\\\ \n",
    "& \\;\\;\\;\\;\\;\\;\\;\\;\\; + c_2 \\times \\text{ (approximate frequency of $c_2$) } \\\\\\\\[5pt]\n",
    "& \\approx c_1 \\times P(Z = c_1) + c_2 \\times P(Z = c_2 ) \\\\\\\\[5pt]\n",
    "& = E[Z]\n",
    "\\end{align}\n",
    "\n",
    "\n",
    "Equality holds in the limit, but we can get closer and closer by using more and more samples in the average. This Law holds for almost *any distribution*, minus some important cases we will encounter later.\n",
    "\n",
    "##### Example\n",
    "____\n",
    "\n",
    "\n",
    "Below is a diagram of the Law of Large numbers in action for three different sequences of Poisson random variables. \n",
    "\n",
    " We sample `sample_size = 100000` Poisson random variables with parameter $\\lambda = 4.5$. (Recall the expected value of a Poisson random variable is equal to its parameter.) We calculate the average for the first $n$ samples, for $n=1$ to `sample_size`. "
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Ntm9SXc97DSAb6OvlWM7Ggr38DvCFW5qAYgQqA6eBO9zW9wN+c1s3G+vG/l3gJ+Bql22O\nxsItLuuuwWocPOwS/zy3Y4ba+Sfay+uAT7zE66nMt9jn7moP8S62/x4CnACqumxvio9/p0AX+99I\nDZd1PwCveNmnun3ctm710e/Ggj/1UX/601/Z/OmYBaVUIP7jvkJEhmLdLNbDejpcgaJjDTKMMb+6\nLttpamDdlDcBPnfbJ8XtOE2Ar40xzoHBxpgfROQ41k1Sir36oDHmN5f9DgGH3PI/ZOftkTHGiMhc\nrBvMN+zVfbGeAjvKXRmrcXIPEAFUtH9r3A63qbh8XI5VD6sx1MaOKwjrpjfaLenXbstfYXcH8yAB\n6/yliojreayA1XgC62ZuL/CzWAOXv8C6Gc3yFbObJsA21/NujDkiIjuxrs2FCDTGpljnLrlwsQkG\nKopIuMu+TwBbsa7zTcbzGArnOTfGHBOR7fxeptZArIi47xeK9eYArOvwjO9iFvJH+xgZbmUIwXrC\nD9YbqO3GmBMu8f1o/3vwZjXwC9ablcki0gqrPM4uTCISj/W2JB74H6x6ZLDq48YAy+LgT31USpVB\n2lhQSgXitOuC3Uf6LaxuDOuwnnQmYXUncpXjtuyY5agkxk2533iYYtb5ynsOMFZEmttprwdc+3pP\nBO4FnsK6gTsNvInVVcfVaXxbjtVdajhWN5ccrIZARW87+RCEVc62wBm3bQbAGHNaRBKAm7C6yzwK\n/F1EbjXGfHcBeV805xGj47r2AnZ72O7aaIwDamOdjzisbjyBCMJ6K/ZXijaQA21wuR/3GFajwf24\n7v+WAmKMKRCRj4D+WG/Y+gP/McbsBGcjeBWwHhgIHLZ33Yb3+mg8xBri8rfP+qiUKpu0saCUuhDt\ngc3GmCmOFSIScx7H2YY1lsDVzRS+ifgRGCgiFRxvF+zBptWwng5fVMaYbSKyGetmSoBNxpgdLkna\nAx8ZY5LtWARoiPXWwm8iUh3rKfFoY8xqe11dPL/5aAO87bJ8E9a588TxRiPaGLOiuPyNMQbrrUwK\n8IKIbMN66lxcYyEH6ym9qx+BYSJS3fEGR0RqAo2ACcXl7eexA43xR6y+/bHGmCKD1B1EJAxr7MU8\nrLEv/yciG40xaW5J2wBf2vtcg3WtptvbUoHmxpi9Xsq0CbgDq1Htiacyp2J1eapsjCnu+m4DhopI\nVcfbBRFpivXvwZcPgDH2G4QHgBddtl2H9TbhOZcGRDt8z0x2BKvhhb1PKNYbJ8f59Ks+KqXKHp0N\nSSl1IXYC14s101F9e3aZ7r52srnefEwC2orIKyISZ8/6Mtot/VtYT+3fF5GmInIz1tP/tcaYDRdY\njuLMwbopfRDrBsvVTuA+EWktIk2wBiV7naGnGL9hdQsZape9LdYNbLaHtPeIyOMi0kBEnsDqOjLR\n00GNMT9h9al/R0T62jMANReRh0VkLIB93Z4UkVYiEmmf97pYN9zF2Qsk2Nc7XEQq2PEeBT4WkZb2\nm4AFWG9JFgZwLvYCjUWkiX3sioHGaIw5DbwGvCYiw8WapamJiPQWkb+5JJ2G9f+BI4wxU7HejC0Q\ne8YkF38XkfYicj1WfTiB1cjAzuc6sWbPai3WDFmdRGSy3bUMrO5ld4nIJBG53o5ngMvsQkXKbIz5\nAvg3sFhE7hNr5qRWIjJCRAbb+83DGifykX1d22CNafBUb9zP0Y9YA43fxWpcLHDZvA84B4y0r3Fn\nrDcQBT4O+znwqFgzljXDqnvONxH+1EelVNmkjQWllL88dRWYgdUN411gM1Yf7ucDPZ4xZjPWTXlv\nrMGW47AGSuOS5gjWE9q6WN1FltppvU4XeYHmAeHAH/j9BtHhKawbqy+w+oGnA/90S1Nc9wrXshus\nLjOxWE+438VqPGV62OclrK44W4BngbHGmKVe8htqH2s81s3151hvShxPe3/D6kq1Eqvx8zfgZWPM\n+8XEDdYYjqN2DEewBqCfxZpB6hzWbElrsG6q73IdY+KH2VjjYjbYx37gfGI0xryC1dgcgnVTvB6r\nPu0FZ/e5PkBvY4yjS8xArLEnr7kcKh/r3M3AqnPXAnfb5cV+09QOa6zOZ1jneAbWTEXH7DSrgbuB\nG7DGP3yDdQ0cXeM8lRm7zIuxurZtx5rN626sgdjYcd+FNfj4G6x/h2/ax/DHB1gzHS13G2uShTU+\n5zbgv8DfgTEUbSy417Wn7fSfYXWrW0vRbl2+6qNSqgwS6/+nSjEAkSCsV67pxphED9tvwfqPSwjw\nizGmk73+Z6wpCQuAXGPMDZcqZqWUUuWbiAwA3jHGXMi4EaWUuuyVhTELo7D6XroPCkREqgH/wJoC\n76CI/I/L5gKsKe1+c99PKaWUUkopdeFKtRuSPYjvbmBWMUn6AMnGmIMAxpijrruj3aiUUkoppZQq\nMaV9sz0JGEvx/XobAtVFZI2I/EdE+rlsM8Bqe/3Qkg5UKaXUlcMY84F2QVJKqVLshiQiXYHDxpjv\n7XEJnqZlqwC0Am7FGkC20Z7abg/WB3QyReRarEbDdmNMiodjKKWUUkoppc5DaY5ZuAlIFJG7sb62\nebWIzDHG9HdJkw4ctWeeOCsi67Bmb9hjjMkEMMb8IiL/D2umiSKNhcTERHP27Flq1aoFQJUqVWjQ\noAHx8fEAfP/99wC6rMvOv8tKPLpctpe1vuiyv8uOdWUlHl0u28uOdWUlHl0uO8t79uzh9GnrO5+H\nDh0iNjaW6dOn+/oGygUr9dmQAESkIzDGfTYkEWmMNRf2nUAo1vRwvYGfgSBjzCkRqQL8C3jRGPMv\n92P379/fRF99N6Nf6VLCpVCXu7/97W88++yzpR2GukxofVH+0rqiAqH1Rflr1KhRzJkzp8QbC2Vh\nNqRCRGQY1tTjM40xO0RkFdZc6vnATPurqjHA/xMRg1WGjzw1FMBqeUVdfcnCV5ex/fv3l3YI6jKi\n9UX5S+uKCoTWF1XWlInGgjFmLdYHXDDGzHDbNhG3L5QaY/YC8QEc/yJEqZRSSiml1JWltGdDKnFd\nunQpfq4lpVz06dOntENQlxGtL8pfWldUILS+KH+1aNHikuRT7hsLjoEhSvly8803l3YI6jKi9UX5\nS+uKCoTWF+WvS3WPWya6IZUkazR57dIOQ10GUlJS9D/Sym9aX5S/yltdOXXqFMePH0ekxMdVXpGO\nHz9OtWrVSjsMVUYEBwdTo0aNUv33Vu4bC0oppZS6OLKysgCoXbu2NhZKSO3a+oBT/S47O5sjR45Q\ns2bNUovhiumGpIOclS/l6cmfKnlaX5S/ylNdOXfuHOHh4dpQUOoSCQsLIz8/v1RjKPeNBSdtKyil\nlFJKKRWQct9YcHwBT9sKypeUlCIfAFeqWFpflL+0riilLmflvrHgpN2QlFJKKaWUCki5byz8Pmah\nlANRZV556lesSp7WF+UvrStKqctZuW8sOGhbQSmllFLlzZ49e+jYsSPR0dG88847pR3ORRcfH8+6\ndetKO4wrWrlvLDjHLOirBeWD9itWgdD6ovyldUWVpKlTp9K+fXv27dvH0KFDSzsc5Ydjx47Rr18/\nIiMjiY+PJzk5ubRD8qrcNxactK2glFJKqQtU2tNYujtw4ACNGzcu7TBUAJ5++mlCQ0PZtWsXb7/9\nNmPGjGHnzp2lHVaxyn1jwTlmQVsLygftV6wCofVF+UvryqU1ZcoUEhISiIqKol27dixfvhywnsAP\nHDiwUNpnn32WP/3pTwAcOnSIAQMG0LBhQ1q1asXMmTOd6eLj451P8CMjIykoKCg2H4ctW7Zwyy23\nEB0dzcMPP8zgwYN57bXXfOblbteuXSQmJhITE8NNN93EZ5995tzWrVs3UlJSGDduHFFRUaSlpV3Q\nuXM3ZcoUmjZtSlRUFDfeeCPr1693ri+u7PHx8UybNo327dsTFRXFqFGj+OWXX0hKSiIqKooePXpw\n4sSJQuknT55M27ZtiY2N5YknniAnJ8djPN7OW3GxAowdO5Zx48YFVEZf+f3www906tSJ6OhoBg8e\nzJAhQ5zX15vs7GyWLVvGc889R+XKlWnTpg133303Cxcu9LlvablyvuCsbQWllFKqRE3fmM5PWWcu\n+Dix4ZV5rG3d89o3JiaGlStXUqNGDZYsWcKjjz7Kpk2b6NGjBxMmTOD06dNUqVKFgoICli5dyty5\nczHG0KdPH7p27cq7777LwYMH6d69O3FxcXTq1AmAxYsXs3DhQqpXr05QUFCx+dSoUYPc3Fz69+/P\niBEjGDRoECtXrmTIkCGMHDnSr7wc8vLy6NOnD/369WPx4sVs3LiRhx56iDVr1hAbG8uSJUtITEwk\nKSmJvn37XvB5d7Vnzx5mzZrFmjVrqFGjBunp6c63Kt7KDrBs2TKWLFlCbm4uHTt2ZOvWrUybNo24\nuDiSkpKYMWMGY8eOdea1aNEiFi9eTFhYGA888AATJ05k/PjxheLxdt4iIyOLjRVgwoQJAZfRW343\n33wz/fr1Y/jw4QwZMoTly5czdOhQRo0a5fO8/vTTT4SEhBATE+Nc17RpUzZs2ODnlbn0yv2bhd/H\nLJRyIKrM037FKhBaX5S/tK5cWomJic6b1m7dulG/fn02b95M3bp1ad68ufMp+Nq1awkLC6NVq1Zs\n2rSJrKwsxowZQ3BwMFFRUc4bdIdhw4YRERFBaGio13wAUlNTyc/PZ+jQoQQHB3PPPffQqlUrADZv\n3uwzL4fU1FSys7MZNWoUFSpUoH379nTp0uW8+7hv2bKF2bNn8+qrr7JixQqWLl3KiBEjPKYNDg4m\nNzeX7du3k5eXR926dYmOjvZZdoBHHnmE8PBwatWqRZs2bUhISKBp06ZUrFiRrl27snXr1kJ5DR06\nlIiICKpVq8bo0aM9ls/befMWqzfe9isuv+TkZFJTU8nLy2PYsGEEBweTmJhIy5Yt/boGp0+f5uqr\nry607uqrr+bUqVN+7V8arpw3C/pqQSmllCpR5/s24GJasGAB06dPZ//+/YDV7SMrKwuAnj17kpyc\nTFJSEsnJyfTs2ROA9PR0MjMzqV+/PmA9VS4oKKBdu3bO49auXdvvfDIzM4mIiCiUvk6dOoA1xsBX\nXg6ZmZlF8o2MjCQzM/M8zgwcPXqUuLg41q5dy3PPPQfACy+84DFtTEwMr776Kq+//jo7d+7k1ltv\n5ZVXXqFmzZpeyw5w7bXXOv+uXLlyoeVKlSoVuTF2LWNkZCSHDx8uEo+38+Yp1pdffplatWp5PR/e\n9isuv7Zt23q8vpGRkV7zcqhSpQonT54stO7EiRNcddVVfu1fGsr9mwX9zoLyl/YrVoHQ+qL8pXXl\n0klPT+epp55iwoQJ7N27l71799K4cWPnjIj33XcfX331FRkZGSxfvpxevXoB1o18vXr1SEtLIy0t\njb1797Jv3z7mz5/vPLaI+J1PrVq1itzQHzx40O+8HCIiIsjIyChSRvcbVX917tyZL7/8kvvvvx+A\nb7/9lmbNmhWbvmfPnqxYsYItW7YA8OKLL/os+/lwnBuwGgWebvJ9nTf3WF966SW/8i5uv+LyW7Bg\ngcfrm56e7ld+sbGx5OXlsXfvXue6H3/8sUwPUi/3jQUHnTpVKaWUKt9Onz5NUFAQ4eHhFBQU8NFH\nH7F9+3bn9vDwcNq1a8eIESOoV68ecXFxACQkJHDVVVcxdepUzp49S35+Ptu3b3d2ZQ40n9atWxMc\nHMysWbPIz89nxYoVzm46xeX13XffFcknISGBypUrM3XqVPLy8khJSWHVqlX06NHjvM/RunXr6Nix\nI2C9HenduzerVq0qkm7Pnj2sX7+enJwcKlasSKVKlRARn2U/H7NnzyYjI4PffvuNSZMm0b179yJp\nvJ234mJ1ePzxxz12t/K2n7f8WrduTYUKFZg5cyZ5eXl8+umnhbpheRMWFsY999zDX//6V7Kzs/n6\n66/57LPPSEpKOs+zV/LKfWOhuH/oSrnTfsUqEFpflL+0rlw6jRo1Yvjw4dxxxx00btyYHTt20KZN\nm0JpevXqxbp165xvFQCCgoKYP38+W7dupWXLljRs2JAnn3zSOWuP642nP/mEhIQwZ84cPvzwQ2Ji\nYli0aBFdunQhNDS02Lzcu6Y4jjNv3jxWr15NgwYNGDduHG+//TYNGjRwpnGPzZszZ85wzTXXULVq\nVcDqEnPujyT6AAAgAElEQVT8+PFC3YQccnJyePHFF4mLi6NJkyZkZWXxl7/8xWfZ3ePxJ75evXrR\ns2dPEhISqF+/PmPGjCmyv7fzVlysDhkZGUXqgbcy+srPcX3nzZtHbGwsn3zyCffee2+hYyclJTF5\n8mSP5Z0wYQJnzpyhUaNGDBs2jDfeeINGjRr5PE+lRcr7E/c33njD/LqnNsPH30rYVRVLOxxVhqWk\npGh3AeU3rS/KX+WprmRkZBTpQ6/8c/vttzNo0CAefPDB0g6lTHFMS9uhQ4cSOX5ubi4dOnQgJSWF\n4ODgEskDrLcXderUKTKL08VQ3L+7zZs307lzZ/9bi+ep3L9Z0O8sKH+Vl/8zV5eG1hflL60rV6YN\nGzZw5MgR8vPzmT9/Ptu3b6dz586lHdYVJyQkhI0bN5ZoQ6G8u3JmQ9K2glJKKaUukd27dzNo0CCy\ns7OpV68e77//vnO6UfW7QLpRlWXlpRyelPvGgjVmobYOcFY+laeuAqrkaX1R/tK6cmUaMGAAAwYM\nKO0wyjxPA7svR2+99VZph1Biyn03JKWUUkoppdT5KfeNBf3OgvKXPvlTgdD6ovyldUUpdTkr940F\nB+2GpJRSSimlVGDKfWNBv7Og/KVzoatAaH1R/tK6opS6nJX7xoKDvllQSimllFIqMOW+saBjFpS/\ntF+xCoTWF+UvrStKqctZuW8sKKWUUkoppc5PuW8sOMYsaDck5Yv2K1aB0Pqi/KV1RSl1OSv3jQUn\nbSsopZRSSikVkHLfWHCOWSjlOFTZp/2KVSC0vih/aV1RJWnPnj107NiR6Oho3nnnndIO56KLj49n\n3bp1pR3GFa3cNxYcTIE2F5RSSilVvkydOpX27duzb98+hg4dWtrhKD/MmjWLzp07ExERwYgRI0o7\nHJ/KfWNBv7Og/KX9ilUgtL4of2ldKV/y8/NLO4RCDhw4QOPGjUs7DBWAiIgInn76afr27Vvaofil\n3DcWHHR8s1JKKVX+TZkyhYSEBKKiomjXrh3Lly8HrCfwAwcOLJT22Wef5U9/+hMAhw4dYsCAATRs\n2JBWrVoxc+ZMZ7r4+HjnE/zIyEgKCgqKzcdhy5Yt3HLLLURHR/Pwww8zePBgXnvtNZ95udu1axeJ\niYnExMRw00038dlnnzm3devWjZSUFMaNG0dUVBRpaWkXdO7cTZkyhaZNmxIVFcWNN97I+vXrneuL\nK3t8fDzTpk2jffv2REVFMWrUKH755ReSkpKIioqiR48enDhxolD6yZMn07ZtW2JjY3niiSfIycnx\nGI+381ZcrABjx45l3LhxAZXRV34//PADnTp1Ijo6msGDBzNkyBDn9fWla9eu3HXXXVxzzTV+pS9t\nFUo7gJIWHx/PF3uO8P6UFIY/dythVSqWdkiqjNJ+xSoQWl+Uv66kurL9fydz4r+7L/g4VZvFcd3L\nT57XvjExMaxcuZIaNWqwZMkSHn30UTZt2kSPHj2YMGECp0+fpkqVKhQUFLB06VLmzp2LMYY+ffrQ\ntWtX3n33XQ4ePEj37t2Ji4ujU6dOACxevJiFCxdSvXp1goKCis2nRo0a5Obm0r9/f0aMGMGgQYNY\nuXIlQ4YMYeTIkX7l5ZCXl0efPn3o168fixcvZuPGjTz00EOsWbOG2NhYlixZQmJiIklJSRf9KfWe\nPXuYNWsWa9asoUaNGqSnpzvfqngrO8CyZctYsmQJubm5dOzYka1btzJt2jTi4uJISkpixowZjB07\n1pnXokWLWLx4MWFhYTzwwANMnDiR8ePHF4rH23mLjIwsNlaACRMmBFxGb/ndfPPN9OvXj+HDhzNk\nyBCWL1/O0KFDGTVq1EW9BmXFFfNmAeDXI6dKOwSllFJKlaDExETnTWu3bt2oX78+mzdvpm7dujRv\n3tz5FHzt2rWEhYXRqlUrNm3aRFZWFmPGjCE4OJioqCjnDbrDsGHDiIiIIDQ01Gs+AKmpqeTn5zN0\n6FCCg4O55557aNWqFQCbN2/2mZdDamoq2dnZjBo1igoVKtC+fXu6dOlCcnLyeZ2bLVu2MHv2bF59\n9VVWrFjB0qVLi+0zHxwcTG5uLtu3bycvL4+6desSHR3ts+wAjzzyCOHh4dSqVYs2bdqQkJBA06ZN\nqVixIl27dmXr1q2F8ho6dCgRERFUq1aN0aNHeyyft/PmLVZvvO1XXH7JycmkpqaSl5fHsGHDCA4O\nJjExkZYtW/p3ES5Dpf5mQUSCgFQg3RiT6GH7LcAkIAT4xRjTyV5/JzAZq8Ez2xjzuqfjW2MWalvH\nCpISKIEqL1JSUq6oJ4Dqwmh9Uf66kurK+b4NuJgWLFjA9OnT2b9/PwDZ2dlkZWUB0LNnT5KTk0lK\nSiI5OZmePXsCkJ6eTmZmJvXr1wesp8oFBQW0a9fOedzatWv7nU9mZiYRERGF0tepUwewxhj4yssh\nMzOzSL6RkZFkZmaex5mBo0ePEhcXx9q1a3nuuecAeOGFFzymjYmJ4dVXX+X1119n586d3Hrrrbzy\nyivUrFnTa9kBrr32WufflStXLrRcqVIlTp0q/PDWtYyRkZEcPny4SDzezpunWF9++WVq1arl9Xx4\n26+4/Nq2bevx+kZGRnrN63JWFt4sjAK2edogItWAfwD3GGOaAffb64OAt4AuQFPgQRHxObpHtK2g\nlFJKlVvp6ek89dRTTJgwgb1797J3714aN27s/DDrfffdx1dffUVGRgbLly+nV69egHUjX69ePdLS\n0khLS2Pv3r3s27eP+fPnO48tLjcRvvKpVatWkRv6gwcP+p2XQ0REBBkZGUXK6H6j6q/OnTvz5Zdf\ncv/99wPw7bff0qxZs2LT9+zZkxUrVrBlyxYAXnzxRZ9lPx+OcwNWo8DTTb6v8+Ye60svveRX3sXt\nV1x+CxYs8Hh909PTAy/4ZaJUGwsiUhe4G5hVTJI+QLIx5iCAMeaovf4GYLcxZp8xJhdYANzn6QCO\n7yzYOV6UuFX5dKU8+VMXh9YX5S+tK5fO6dOnCQoKIjw8nIKCAj766CO2b9/u3B4eHk67du0YMWIE\n9erVIy4uDoCEhASuuuoqpk6dytmzZ8nPz2f79u3FzqjoK5/WrVsTHBzMrFmzyM/PZ8WKFc5uOsXl\n9d133xXJJyEhgcqVKzN16lTy8vJISUlh1apV9OjR47zP0bp16+jYsSNgvR3p3bs3q1atKpJuz549\nrF+/npycHCpWrEilSpUQEZ9lPx+zZ88mIyOD3377jUmTJtG9e/ciabydt+JidXj88cc9drfytp+3\n/Fq3bk2FChWYOXMmeXl5fPrpp4W6YfmSn5/P2bNnKSgoID8/n3PnzpW5WbZclfabhUnAWIr/ZlpD\noLqIrBGR/4hIP3t9HeCAS7p0e51X+mZBKaWUKr8aNWrE8OHDueOOO2jcuDE7duygTZs2hdL06tWL\ndevWOd8qAAQFBTF//ny2bt1Ky5YtadiwIU8++aRz1h5xu4HwlU9ISAhz5szhww8/JCYmhkWLFtGl\nSxdCQ0OLzevkyZNFyhMSEsK8efNYvXo1DRo0YNy4cbz99ts0aNDAmcY9Nm/OnDnDNddcQ9WqVQGo\nUqUKx48fL9RNyCEnJ4cXX3yRuLg4mjRpQlZWFn/5y198lt09Hn/i69WrFz179iQhIYH69eszZsyY\nIvt7O2/FxeqQkZFRpB54K6Ov/BzXd968ecTGxvLJJ59w7733Fjp2UlISkydP9ljeiRMnUqdOHaZM\nmcI///lP6tSpwxtvvOHzPJUWuZDXRheUsUhX4C5jzAh7XMIYY8y9bmmmAQnArUAVYCPWm4gWQBdj\nzCN2ur7ADcaYke75JCYmmp93HqPa1dfSvHVd6kTW5Prrr3c+6XHMf63Luuw6F3pZiEeXy/ay1hdd\n9nfZsa6sxHMhy+Hh4Vx33XWowN1+++0MGjSIBx98sLRDKVMc09J26NChRI6fm5tLhw4dSElJITg4\nuETyAOvtRZ06dYrM4nQxZGRkkJaWxtatWzl+/DgA+/fv549//CNjxowp8UfhpdlYeA3oC+QBlYGr\ngcXGmP4uaZ4BKhljXrSXZwErgYPAC8aYO+31zwLG0yDnN954w/y6xxo403d4W2rVrVai5VKXrytp\nEKK6cFpflL/KU13JyMgoMuBWebZhwwYaNGhAeHg4CxcuZOzYsWzevNk5i5CylHRj4VIp6caCp393\nmzdvpnPnziXeWCi1bkjGmPHGmChjTH3gAeAL14aC7RPgZhEJFpEw4EZgO/AfoIGIRItIRXv/pZ7y\nKTRmQbshKS/Ky/+Zq0tD64vyl9aVK9Pu3bvp0KEDMTExTJ8+nffff18bCh4E0o2qLCsv5fCk1KdO\ndSciw7DeEsw0xuwQkVXAD0A+MNMYs81ONwL4F79Pnep7dI1+xVkppZRSl8CAAQMYMGBAaYdR5nka\n2H05euutt0o7hBJT2gOcATDGrHV8Y8EYM8MYM9Nl20RjTFNjTHNjzDSX9Z8ZYxoZY+KMMX8r7tiu\nMxmUVpcrdXlw7V+slC9aX5S/tK4opS5nZaKxcKloY0EppZRSSin/lfvGguuYBW0rKG+0X7EKhNYX\n5S+tK0qpy1m5byy4KijQ1oJSSimllFL+KveNBR2zoPyl/YpVILS+KH9pXVFKXc7KfWPBlSko7QiU\nUkoppZS6fJT7xkLhMQv6ZkEVT/sVq0BofVH+0rqilLqclfvGgittLCillFJKKeW/ct9YKDxmoRQD\nUWWe9itWgdD6ovyldUUpdTkr940FV/pmQSmllFLlyZ49e+jYsSPR0dG88847pR3ORRcfH8+6detK\nO4wrWrlvLBQas6BTpyovtF+xCoTWF+UvrSuqJE2dOpX27duzb98+hg4dWtrhKB9ycnIYOXIkLVq0\nIDo6mltuuYXPP/+8tMPyqtw3FlzpiwWllFJKXYj8/PzSDqGQAwcO0Lhx49IOQ/kpLy+PunXrsnz5\ncvbt28f48eMZNGgQ6enppR1ascp9Y8F1zIJ+lE15o/2KVSC0vih/aV25tKZMmUJCQgJRUVG0a9eO\n5cuXA9YT+IEDBxZK++yzz/KnP/0JgEOHDjFgwAAaNmxIq1atmDlzpjNdfHy88wl+ZGQkBQUFxebj\nsGXLFm655Raio6N5+OGHGTx4MK+99prPvNzt2rWLxMREYmJiuOmmm/jss8+c27p160ZKSgrjxo0j\nKiqKtLS0Czp37qZMmULTpk2JiorixhtvZP369c71xZU9Pj6eadOm0b59e6Kiohg1ahS//PILSUlJ\nREVF0aNHD06cOFEo/eTJk2nbti2xsbE88cQT5OTkeIzH23krLlaAsWPHMm7cuIDK6Cu/H374gU6d\nOhEdHc3gwYMZMmSI8/p6ExYWxrhx46hbty4Ad9xxB9HR0YXuV8uaCqUdwKWkYxaUUkqpkvPFsu0c\nyTzhO6EPNSKqcus9153XvjExMaxcuZIaNWqwZMkSHn30UTZt2kSPHj2YMGECp0+fpkqVKhQUFLB0\n6VLmzp2LMYY+ffrQtWtX3n33XQ4ePEj37t2Ji4ujU6dOACxevJiFCxdSvXp1goKCis2nRo0a5Obm\n0r9/f0aMGMGgQYNYuXIlQ4YMYeTIkX7l5ZCXl0efPn3o168fixcvZuPGjTz00EOsWbOG2NhYlixZ\nQmJiIklJSfTt2/eCz7urPXv2MGvWLNasWUONGjVIT093vlXxVnaAZcuWsWTJEnJzc+nYsSNbt25l\n2rRpxMXFkZSUxIwZMxg7dqwzr0WLFrF48WLCwsJ44IEHmDhxIuPHjy8Uj7fzFhkZWWysABMmTAi4\njN7yu/nmm+nXrx/Dhw9nyJAhLF++nKFDhzJq1KiAz/ORI0dIS0sr02+Hyv2bBf3OgvKX9itWgdD6\novyldeXSSkxMdN60duvWjfr167N582bq1q1L8+bNnU/B165dS1hYGK1atWLTpk1kZWUxZswYgoOD\niYqKct6gOwwbNoyIiAhCQ0O95gOQmppKfn4+Q4cOJTg4mHvuuYdWrVoBsHnzZp95OaSmppKdnc2o\nUaOoUKEC7du3p0uXLiQnJ5/XudmyZQuzZ8/m1VdfZcWKFSxdupQRI0Z4TBscHExubi7bt293dp2J\njo72WXaARx55hPDwcGrVqkWbNm1ISEigadOmVKxYka5du7J169ZCeQ0dOpSIiAiqVavG6NGjPZbP\n23nzFqs33vYrLr/k5GRSU1PJy8tj2LBhBAcHk5iYSMuWLf27CC4cx3jwwQdp0KBBwPtfKlfYm4XS\njkAppZQqv873bcDFtGDBAqZPn87+/fsByM7OJisrC4CePXuSnJxMUlISycnJ9OzZE4D09HQyMzOp\nX78+YD1cLCgooF27ds7j1q5d2+98MjMziYiIKJS+Tp06gDXGwFdeDpmZmUXyjYyMJDMz8zzODBw9\nepS4uDjWrl3Lc889B8ALL7zgMW1MTAyvvvoqr7/+Ojt37uTWW2/llVdeoWbNml7LDnDttdc6/65c\nuXKh5UqVKnHq1KlCebmWMTIyksOHDxeJx9t58xTryy+/TK1atbyeD2/7FZdf27ZtPV7fyMhIr3m5\nM8YwbNgwQkNDef311wPa91Ir928WCn1nQccsKC+0X7EKhNYX5S+tK5dOeno6Tz31FBMmTGDv3r3s\n3buXxo0bO3sW3HfffXz11VdkZGSwfPlyevXqBVg38vXq1SMtLY20tDT27t3Lvn37mD9/vvPYIuJ3\nPrVq1SpyQ3/w4EG/83KIiIggIyOjSBndb1T91blzZ7788kvuv/9+AL799luaNWtWbPqePXuyYsUK\ntmzZAsCLL77os+znw3FuwGoUeLrJ93Xe3GN96aWX/Mq7uP2Ky2/BggUer2+gA5SfeOIJfv31V+bM\nmUNwcHBA+15q5b6x4Eq7ISmllFLl1+nTpwkKCiI8PJyCggI++ugjtm/f7tweHh5Ou3btGDFiBPXq\n1SMuLg6AhIQErrrqKqZOncrZs2fJz89n+/btxQ469ZVP69atCQ4OZtasWeTn57NixQpnN53i8vru\nu++K5JOQkEDlypWZOnUqeXl5pKSksGrVKnr06HHe52jdunV07NgRsN6O9O7dm1WrVhVJt2fPHtav\nX09OTg4VK1akUqVKiIjPsp+P2bNnk5GRwW+//cakSZPo3r17kTTezltxsTo8/vjjHrtbedvPW36t\nW7emQoUKzJw5k7y8PD799NNC3bB8GT16NLt37+ajjz6iYsWK53HGLq1y31goPGahFANRZZ72K1aB\n0Pqi/KV15dJp1KgRw4cP54477qBx48bs2LGDNm3aFErTq1cv1q1b53yrABAUFMT8+fPZunUrLVu2\npGHDhjz55JPOWXtcbzz9ySckJIQ5c+bw4YcfEhMTw6JFi+jSpQuhoaHF5nXy5Mki5QkJCWHevHms\nXr2aBg0aMG7cON5+++1C/dvdY/PmzJkzXHPNNVStWhWAKlWqcPz48ULdhBxycnJ48cUXiYuLo0mT\nJmRlZfGXv/zFZ9nd4/Envl69etGzZ08SEhKoX78+Y8aMKbK/t/NWXKwOGRkZReqBtzL6ys9xfefN\nm0dsbCyffPIJ9957b6FjJyUlMXny5CJ5pqen88EHH/Df//6Xxo0bExUVRVRU1HmPQ7kUpLw/bf/3\nv/9tvlh0BIA7ujeleevA+pQppZRSypKRkVGkD73yz+23386gQYN48MEHSzuUMsUxLW2HDh1K5Pi5\nubl06NCBlJSUEu3u8/jjj1OnTp0iszhdDMX9u9u8eTOdO3f2v7V4nsr9mwUds6D8pf2KVSC0vih/\naV25Mm3YsIEjR46Qn5/P/Pnz2b59O507dy7tsK44ISEhbNy4scyPCyjLrqjZkLStoJRSSqlLYffu\n3QwaNIjs7Gzq1avH+++/75xuVP0ukG5UZVl5KYcnV1Q3pFvvvY5WbX3Pu6uUUkqporQbklKXnnZD\nuoR8dUPKOZfH6ZPnLlE0SimllFJKlW3lvrFQaMyCh7bC2TO5bP/emsN43oyvmf7XNZcqNFXGaL9i\nFQitL8pfWleUUpezct9YcOWpy9Vni7ayfOEPZB05xdFD1hcFC9zeQJzJziFl9e4i65VSSimllCrP\nyn1jofB3Fore7J88fhaA3Jx857ozp3MKpdn+fQZfr/mJo4eKzoGsyg+dC10FQuuL8pfWFaXU5Syg\nxoKIdBKRGPvvCBH5QETeE5Gi3+UugzyO5RbHtt83njpxtlCSQwetj7LknMsrqdCUUkoppZQqcwJ9\ns/B/gOMR/BtACFAAzLyYQV1Mvr6z4JjqyrUhccptkPOh9OMA5Li8fVDlj/YrVoHQ+qL8pXVFKXU5\nC/Q7C3WMMftFpALQBYgGcoCMix5ZCfDUDckxLe6BtCznulPHf3+zcO5sHr8ePQ1Azll9s6CUUkop\npa4cgb5ZOCEiNYGOwDZjzCl7fcjFDevicR2z4GmAsuPNwvp/7Xauc32zcCTjBNi75eRoY6E8037F\nKhBaX5S/tK6oy0l8fDzr1q276McNDw/n559/vujHVSUv0MbCNOA/wEfAP+x1NwE7LmZQJcXTmAVP\nH9zLOnLK+fehg8edf5/TNwtKKaVUmVVSN7qXi6+++opmzZqVdhgelecvHJd3ATUWjDGvA7cBNxlj\nFtirDwJDLnZgF4vrmAWvI5xd7NuTRUF+AQCHDx7n6mqVAB3gXN5pv2IVCK0vyl9aV8qO/PzyPfbQ\nGFNmb8o9dQVXl4fzmTo1GhgvIp/ay1WBay9eSCWnwMuYBYeGzWpy7mwemfag5hPHzvKH8DBCKgZr\nY0EppZQqox577DHS09Pp06cPUVFRTJs2jQMHDhAeHs7cuXNp3rw53bp18/j03fWNhDGGyZMnk5CQ\nQFxcHIMHD+b48eOesgRg1apVdOzYkZiYGO666y62bdsGwM8//0xsbCxbt24FIDMzk4YNG7JhwwYA\nEhMTefnll7ntttuIjo6mX79+hfL5z3/+w5133klMTAwdO3bkq6++cm47duwYI0aMoGnTpsTGxtK/\nf3+ys7Pp3bs3hw4dIioqiqioKA4fPuyzPB9//DEtWrQgLi6ON998s9hybtq0ieuuu67QTf+yZcto\n3749AJs3b6ZLly7ExMTQtGlTnnnmGfLyPN83JSYmMnfuXOfy/Pnzufvuu53Lu3btokePHsTGxnLj\njTeyZMmSYuNSJS/QqVOfAKYDu4EO9uozwCsXOa6LpvB3Fopud2+B1476A4BzUPPZM7lUCgshtFIF\ncs6V7ycSVzrtV6wCofVF+etKqyvVq1f3+Ask/fmYPn06devWZf78+ezfv58nnnjCuW3jxo188803\nLFq0CPDeJWbGjBmsXLmS5cuXs23bNq655hqefvppj2l/+OEHRo4cyeTJk0lLS2PgwIH06dOH3Nxc\n6tWrxwsvvMCwYcM4c+YMI0aMoE+fPrRr1865/8cff8w//vEPduzYQVBQEM888wwAGRkZPPjgg4wd\nO5a9e/fy0ksvMWDAAH799VcAhg0bxtmzZ9m4cSO7du3iscceIywsjIULF1KrVi3279/P/v37qVmz\nptfy7Nixg7FjxzJjxgy2bdvGr7/+SmZmpseyJiQkUKVKlULdvJKTk7n//vsBCA4O5rXXXiMtLY1V\nq1axbt06Zs+e7fO6OTiuSXZ2Nj179iQpKYk9e/Ywe/Zsxo0bx65du/w+lrq4An2z8CRwmzHmb1hT\npoI1XqHRRY2qhJgCQ865PE4cO+Nc5/7fi7AqFUHgxG9WmrNncqlUOYSKFSvomwWllFKqjHPv7iIi\nPPvss1SuXJnQ0FCf+7///vv8+c9/platWoSEhDB27FiWLl1KQUFBkbRz5sxh4MCBtGzZEhGhd+/e\nhIaGkpqaCkC/fv2oX78+t99+O7/88gvPPfdcof179+5No0aNqFy5MuPHj+eTTz7BGMOiRYu44447\n6Ny5MwAdO3YkPj6e1atXc/jwYf7973/z5ptvUrVqVYKDg2nbtu15lefTTz+lS5cutGnThpCQEMaP\nH++1IdW9e3dng+vkyZN8/vnn9OjRA4AWLVqQkJCAiFC3bl0GDBhQ6G2Iv1atWkV0dDQPPPAAIkKz\nZs245557+OSTTwI+lro4Ap069WrggP23419jCNb0qWWSNWahNmD9B2TFP39gz7YjjHzhNipWrFCo\ntVD92irENa3JVatCOXHsLMYYzp3JJbRyCBUraWOhvEtJSbningCq86f1RfnrSqsrjqffJZX+fNSu\nXdvvtOnp6fTr14+gIOt5qjGGkJAQjhw5Qq1ahb9Be+DAAT7++GPeeecdZ9q8vLxCT+f79evHQw89\nxKRJkwgJKTx5ZJ06dZx/R0ZGkpubS1ZWFgcOHGDJkiV89tlnzuPm5+fToUMHDh48SPXq1alateoF\nl+fQoUOFYggLC/P6ZqdXr17cddddvPnmmyxbtowWLVpQt25dAH766Sf+/Oc/8/3333PmzBny8/Np\n0aKFXzG6OnDgAKmpqdSvX79Q2Xv37h3wsdTFEWhjYR3wLPCqy7qRwJqLFlEJMgWQddia6WjnD4e4\n/o91C22/94F4QioGU/Waypw4doa83ALy8439ZkHHLCillFJlWXFPxV3Xh4WFcebM7z0M8vPzycr6\n/VtLderUYdq0adxwww0+86tTpw6jR4/mqaee8rj99OnTjB8/nr59+/L666+TmJhItWrVnNsPHjzo\n/PvAgQOEhIQQHh5OnTp16N27N5MmTSpyzMOHD/Pbb79x4sSJIg0GT+X3Vp6aNWuye/fvU8dnZ2d7\nbbw1atSIyMhIVq9eTXJyMr169XJue/rpp2nevDmzZ88mLCyMt99+m08//dTjcdyvwZEjRwrFe9NN\nN5GcnFxsHOrSCrQb0hNAdxH5GbhaRHYCScDo8w1ARIJEZLOILPWwraOIHLO3bxaRP7ts+1lEtojI\ndyLybXHHLzxmwVAr0vpHemCv9Y/BMesRQEhoMABVr6nEiWNnOHsmF4BKlSoQElqBg/uOcfhg8YOc\n1DZrZW4AACAASURBVOXtSnrypy6c1hflL60rl06NGjWKzOXv3i0pNjaWc+fOsXr1avLy8pg4cSI5\nOb93kBg4cCCvvPIK6enpABw9epSVK1d6zK9///689957bNq0CbAaB6tXr+b0aWvc47PPPkurVq2Y\nPHkyt99+e5FGxcKFC9m1axfZ2dn87W9/47777kNEuP/++1m1ahVffPEFBQUFnD17lq+++orMzExq\n1qzJbbfdxtixYzl+/Dh5eXls3LgRgGuvvdbZkPCnPImJiaxatYpvvvmG3Nxc/vrXv/qctahnz57M\nmDGDr7/+mvvuu8+5/uTJk1x99dWEhYWxa9cu3nvvvWKPcf3117Ns2TLOnDlDWlpaocHOXbp04aef\nfmLhwoXk5eWRm5vLd999p2MWSlGgU6dmAq2xGgh9gAHADcaYQxcQwyhgm5ft64wxreyf60DqAuAW\nY0xLY4zX5v/Tr91J5bAQCozhlxPWfxDOZlsNgXzXxkKI1Vi4JrwKJ46dJd1uUIRWDiG8RhUAUlN+\n5pdDJwuNe1BKKaVU6XvyySeZOHEi9evX5x//sD4H5f60vWrVqkyYMIFRo0bRrFkzrrrqqkLdlB59\n9FHuuusuevbsSXR0NHfeeSebN2/2mF98fDyTJ0/mmWeeoX79+txwww3Mnz8fgJUrV7JmzRomTpwI\nwCuvvMLWrVsLPTHv3bs3w4cPp0mTJs6bdbCers+dO5dJkyYRFxdHixYteOutt5zjJt5++20qVKjA\njTfeSKNGjXj77bcBiIuLo0ePHrRq1Yr69etz+PBhr+Vp3LgxEyZMYOjQoTRp0oTq1av77LLVo0cP\nNmzYQIcOHfjDH/7gXP/yyy/zz3/+k6ioKEaPHk337t0L7ed6HR577DEqVKhA48aNGTFihHOQNMBV\nV11FcnIyixcvpkmTJjRp0oSXXnqJ3Nxcr3GpkiO+WpAicqs/BzLGfBFw5iJ1gfewujWNNsYkum3v\nCDxtjLnXw757gT8aY7Lct7l64403zKBBg/i/V78grmlNVv/3CDWyzxERWY2HHmvLnGlfcSTzJAAj\nn7+NiqEVyD6Vw7uT1hNcIYjTJ8/R6+E/Ui/uf0h+P5UTx846P9r29Gt3ei3fkcwTfPX5Hu6+/3pC\nK5XZj1wr25XWr1hdGK0vyl/lqa5kZGQE1P9fFS8xMZGkpCT69u1b2qGoMq64f3ebN2+mc+fOJf5h\nDX/GLPgz75UB6p9H/pOAsUA1L2naisj3WB9/G2uMcbyFMMBqEckHZhpj3vGWkQQJxhiC7MbR6dNW\nCzUvr+ibhbCrKlIj4mr2p1lvFipVtm70a9apxs+7j/pduM0b9vHT9iP8Z91ebr6jIQAZ+4+x7bsM\n2t3WwJp5SSmllFJKqTLKZ2PBGBNTEhmLSFfgsDHmexG5BU+fUoZNQJQxJlv+P3vvHR7Xcd5t33O2\nV+yiLHolWMAKdlIkRYlUL7bc7dhxjZ3EfpP4TZz4ihMnzueS9jnNRW6xE1u2ZVu2JKsXSqJEsReQ\nBBtAEr0DuwC2tzPvHwdYAARAAhRIQfS5r4sX95ydnZ2zmD07zzzlJ8TdwGPAopHntkgpu4QQeWhG\nwxkp5SSZzNGcBSFAVSVmRXubaEQLR1LTY54VoYxLgHKOlVcbNRZ8ha6pRaCnIRzS3uPMiS623rEI\nKSXPP1pPf0+IWDTJfe+ffZUAnWvHjbLzp3N90OeLzkzR54rOVMxXpWUdnUuZbTWkuWQL8DYhxD2A\nDS1h+sdSyg+PNpBShsY9fkYI8W0hRLaU0j+SP4GUsk8I8SiwAZhkLDzyyCP84Ac/oK9NJeu0nYEw\nlDhLKC9ZilQljU0niIQSlBcvBTR3MYDDlQNAS8dpjh23cuuOW/AVuWnp0Bwbl7Yf/TEYf9zbOay1\n74BwcBOPPXSUI0fHcrHDoTi+BVFMJsOUr9eP9WP9WD/Wj/Xj+XSck5OjhyHNEbpugM5s2LNnDydP\nnsyob7e2trJu3bqMFse15Io5CxMaC2EG/hYtubkQ6AQeBr4qpYxd9SC03IS/mCJnIV9K2TPyeAPw\nSyllhRDCDihSypAQwgE8D/yDlPL5S/sezVn43r/upqTCy8nzAxiCcQD+zxd38t//9hrRsOYBGJ+D\ncGD3RV57rgGjSeGz/3AHoIm6fePLuzIlVC+XsxCNJPjWV16icnEeTef6qFiYQ3PjANvuWEgkkuTI\nnmYA7nnPSpaunvmNt7t9CF+RG0XRdyTmmhsprljn2qPPF52ZciPNFT1nQUfn+vNm5yzMtnTqg8AO\ntBKq69E0Fm4Bvj1XAxJC/KEQ4lMjh+8WQtQLIY4B/wGMKnLkA3tGzu8HnpjKUBiPIrScBTHONopF\nk6RTkxUZQctbADBbxpwvQhH4Cl0j47z8dYSDmgGyYEkeAM2NA5RUeNl4ywKyc+yZdo2newAYCkSv\nqOPQ2znMQ9/ex0tPnrn8m8+QlvP9HN3bPNZ/1zDPP1pPd/tYedjAQJjzZ3qneLWOjo6Ojo6Ojs6N\nzmzDkB4AFkgpB0eOTwshDgDngY9f7SCklLuB3SOPvzvu/LeAb03RvgmovfT8VIzPWZCqRKqStACD\n1MqnjuoslFR4J7zOMZKzYLFM/IgKSrJobw5gMF7ezhrNifDmOCgq89DZOsiiFZryo2ecsdDZOshA\nb4gf/ccelq8t5q53rQBgKBBBSvBkj7Ud1Yao29/K9rsXEwnFyfKOPT8bksk0v/qhJkdvthhZtrqY\nFx47RVfbECcPt/PAh9awoMbHQ9/aRzyW4pN/efNVv9dbhRtl50/n+qDPF52ZciPNFYvFwsDAANnZ\n2XrMvY7OdSASiWAwGN7UMczWWOgG7MDguHM2oGvq5vMHIYSWnKxK4gYD9lSaaCRBKq2y6dYFbL19\n4YT2Fqv20ZgvMRZu2llNJJTgdF0nalpFMUxtNIyGNtkcJu5930r2vNDI0lrNhZQ1YgAoiiAcjLP7\nmXPAmDEA8P1/fRWYGOrU3hzIPP7Rf+xhOBDNlHWdLWdPjP3Jnv11Pc/+uh6AW+9dwqHXmjh5pJ1B\nf4R4TPN2HD/Qxs13LZ71++jo6Ojo3Djk5OQQCoXo7OzUjQUdneuAwWDA5/O9qWOYrbHwE+BZIcQ3\ngHagFPgM8OPxegxXo7lwrairqyPoqaR1KIY7z6EZC0YFeypNKBgHCYYpFvzeXE2EbeMtEyvCmi1G\nfEVuTtd1kkiksdqmMRZGRN9sdjOuLCv3vnes8pEn285H/2wrwaEov/6fI1w81wfAkD/KodeaWLul\nYlw/CWx2M1JK2psDuLKsBIdiDAc0UbjXnmu4KmOhob4Ht9fGJ/58G2eOd3FsbwsFpVmsuakcf1+Y\n4wfbOH+6l6rFeSSTaRpO9bDtzkU39I/DjRRXrHPt0eeLzky50eaK0+nE6XS+2cO4YbnR5ovOW5/Z\nGgt/OPL/Fy45/0cj/+DqNReuGU+c7seBIBxPIaQkajTiJZlZcE8VUmR3mKdNYDZbNHdQIp7KlFW9\nlNEwJJt96udz853YHJOf2/3MuYynAeDYvlZu2llNoD9MNJxgw/ZKDu5uAqBiYQ4t5wdIJFKYzVf+\nU6aSaYwmA+FgnJbz/azZXI7BoLB8TTHL1xRn2i1ZVcjxg22A5mloOT/Ai789jb8vTI7v8j8QUkoO\n7L5Ibr6L6hofqiqJhOLYnRY9KVtHR0dHR0dH5y3GrIyFa6W5cC2pra3lXDdIAepIGFJSEaiKYGjU\nWDDMbhE7GpqUiKenbRMNJzCZDRhN08eZOZwWLFYj8ViKRcvzaajvmdRm767zLFyWT1ebFvlVs7Io\nYyzUrCqiuXGAvq4gxeXeSa8dz8tPneHEoXbyi920N2nhTKs2lk7ZtrQym/XbKhkejOLNdWAyG3j5\nqTM8/csT3Pf+VRmvy9TXnWTP840AfORPttB4uoe9u85jc5h554fXUFjquew430z0nRyd2aDPF52Z\nos8Vndmgzxed+cabqbNw3TCM7GhLVYKUqEKQMhkyOQKXW9BPhcmstU8mpq9eFI0kp/UqjCe/yE3r\nRT9LVxezYXsVWV4b7c0BTh/rZNudi/jhv71Ge5OfrvYhbA4zuQVjO/vl1ZoWRHf70GWNhaFAhCOv\ntwDQ3hSguNxL5aJcvDnTL/q33z2Wn+B0W1lzUwWHXmvif//rdVauL2X7PYunDN/y92WkMfjfb7wO\naJ6bdCrNq8828N5PrJ8gfqejo6Ojo6OjozN/mVXpVCFElhDii0KI3wghnh//71oN8I1SV1eHUREj\nngWJUCVSCBJGA8HBGK4sK9U1s0scmalnweYwX7GvO9+1nEXLCyiryqagOAub3czCpfm8/YOr8ebY\nsTvN7HriDKePdVJS7p2QM+B0W8nOc3D6WCeX08vo79EW8BWLctlx3xI+8Icb2XTrgpleLgDb7ljI\nmpvKSaVUju5roadjeMp2/v7wpHM77qthy20LaWvy8/SvTszqfa8nowJEOjozQZ8vOjNFnys6s0Gf\nLzrzjdl6Fn4FGIBHgejcD+faYFQESUBNqwhAFWQW16s3l+FwWWbV36ix0Nbkz+zuX0pwKIbLY7ti\nX1leO2/7vamrwAohqFiYy+ljnQAUlWshPJ/+wg4YsRnWbC7nxd+epq8riK/IPWU/oSFNL+/OdyzH\nlWW94pimQjEobN6xgKN7NQ9FX3eQorLJIUUDfWGMRoXtdy+m5cIANauKqF7qQ1EE/T0h6o+0c8s9\nS2b9mevo6Ojo6Ojo6Fx/ZivKtgm4W0r5TSnlf4//dy0GNxfU1tZiEAKJQB0RYFOFwJzQvAIV1bOv\nJOT2WFEUwYFXLhAJJSY9H48l6e8NUViS9cYGD9z2tqW87fdq8WTbMx4Qu9OMfcRrMRp+NNWOPkAk\nlKD5/ABCgMN5ZU/H5bDZzfzFV+/EbDHS1xWkvyc0yaMx0BPCm+tg9eZyHvjQGhavKMBgUBBCsHZL\nOVLCuZPdb2gcc0EklJgkgjfbONHRJHad3030uGKdmaLPFZ3ZoM8XnfnGbI2FPcCSazGQa4miCCRk\n1JpV4IzPzZKVBeQVuGbdn81u5l0fXYeUZBKPR5FS8vJTZ0FyxaTjmWC2GFm0vIA/+NzNUyYWu72a\np2B4MDbpuUQ8xbe/9hKNp3q0akTTaELMBiEEeQUu6g608j//uYfXXzyfeU6qkq62QQqmMZJy813k\nFbp46ckzfOefXualJ89cNnxqLmlv8nPqWAdSSpKJNN/+x5d45EeHkaqk8XRPRk/iUqKRBH1dQfq6\ngzz3G03durtjiNeeb+BbX32JE4farsv4dXR0dHR0dHTeDGYbhvRR4OkR1eYJpXuklP/fXA1qLqmr\nq8O4YCcIMmrNUgh6rWbufu+qq062LSr3oCiCjtYAC8blPHR3DFN/pAOAwtI37lm4EharCYvVmCkD\nq6ra4ltRBI2nxv5EsWhyzt5z2ZoiOlq0ikr7X76AJ9vG8rUlDPSFiMdSmXCpqViyspC+riCRUIKj\ne1sor85hwZK5ExsJDsWIx1Lk5o8lgvd2DfPw9w8CaGFUEpCaevbD3z9IR0uAVRtKseUGJuzo+PvD\n/OSbe0kmxnJTTh5uzzw2W4wc3tPMouUFmM0GUimVpoZ+qmt8V1T41nnro9dC15kp+lzRmQ36fNGZ\nb8zWWPgqmhBbMzA+QP76bA9fJcYRz4Ka0oapjtgHibSKTbk6CW2TyUBBSRanjnayakMpWV5NlXmg\nV0smft8nN0xSf75WuL02hgc1Y+HH33wdm83M+z65ga72oUybUa/KXLBiXQlSQkmFl2d/fZJ9L10g\nFk3xytNnASieIpdhlLU3lZPltbFgiY/v/csrvPZcA+mUStUSH8ZZLrCHB6O4x+WFNJ7u4dlHTqKq\nkk/91XZsdi3s6vCeZoQAKckkZq/eVMax/a0Zo+fsiS4WrtP6qdvfisGo0HiqBynhtrcvJRyMoyiC\naDhJWXUOVpuJcDDOEz+v45tf3oXJbMgYFcvWFLPz/hrMFiOJRCrz2Vssxjnx7ujo6Ojo6OjoXC9m\nu5p9P7BIStl1LQZzLaitrWVPdCRnIT2WswAQT6nYZlk2dTy3vW0pP/7mXs6e6Gbjdk2HLtAXRlHE\nlMm/1wq3x0ZzQx/BoRj93Zqxkkym6W4foqTSSyKWoqa2aM7eTwjBqg2aRsPaLRU8+fBxXnn6LL5C\nFxu2V5GdN71wm9FkYMnKQgBqaos4ureF3/6sjlvvXUJJZTaN9d2s2VJB/ZEOVq4vwWozEY+leO43\n9SxbU8SCJT5OHGrjhcdPI1XJ/R+oxeYw8duf1hGLJsktcNLfHeLpX57Ak20nr9BFy/kBlqwqxO2x\n0dES4D0fW4/BqGBzmNm76zzv/MhanvrFcer3pDi7/3lS4wyrm+9aRO3GsmmvZ3hwMX3dWv5GNJzA\n6bZw6mgHXW2DrNpQyt5d5zMhTuu2VXDL3W+5KD6dadB3/nRmij5XdGaDPl905huzNRYuAnMXz3Kd\nGC2dKlNjYUgAifQbc4jkFbowGATxaJL25gAmk4K/P4wn2z6lBsG1wlfo4sKZXr77z69kzj34tZdJ\nxFOs31Y5QTNhrlm0LD/z+PYHls1KdO3muxZTs6qQR350WMvzGOHM8S6GAlGaGvp4x++v4dVnz9FQ\n301DfTfF5R46WsbyRI4fbMNqM2bCrN7zsfWcPdE1oT+AolIPqzeXTzi3eccC1m6pwGI18v5PbeSn\nD+4nldS8Axu3VxEYiLDmporLXsP6bZpOoZQSVZUoiuDciW6efuREZgybdyzgwCsXOXGwnZt2VF83\nj5OOjo6Ojo6OzhtltquWnwC/FUJ8g8k5Cy/N2ajmkLq6OgxL7wAgmUpjYEyxOXaF0BwpJelwBJlK\nY8xyTdA4AG2H3WIzEY0kefh7BzLnF8xSt+GNctOOagpLPTz1i+OoqqRyUR4Wq5HcfOecehSmQjEo\nfPCPN9F4umfaxObpMBoVCks9bLp1AbufOcfaLeU0nOrJKGu3Nfl58B9fJplIs3xtMY2nejKGwu0P\nLCMSSvD6i42Zvu57/yocLgtrt1RQWJpF20U/r42oSRdO4ekRQmCxal+BvAIXK7YZWb1qM8GhGOWz\nrJIlhMjMqyWrCilfmENHyyB2h4miMi9Vi/P46YP7eeRHh7n9gWVXlVivM7/Q44p1Zoo+V3Rmgz5f\ndOYbszUWPjPy/9cuOS+Bqjc+nGuDEFrOgmHEkWA0aqFHiSsYCw1f+w5N3/gJAJ51y9n4xHcnGQxW\nmymTp2B3mvEVulm3pWJOx38lhCKoWpzHZ/5mB6mUet13rgtLPbPyKFzKui0VLK0twuGyULupjLr9\nrSxZWUginuL5x07hzXGw8/6lbLyliv6eEJ2tg9SsKkQogjPHO/H3hbn5rkVULx3zchSVeSkq81JS\nmU3dgVZ8M1icW6wmsvOclw2jmik2u3mC2F9hqYclKws5e6KLn3xrL+//5MbrGqqmo6Ojo6Ojo3M1\niOtVuvLNYteuXfJgwkfTiw3kjdTFb6rKoxGFf79/Icvyp14YynSa54q3TTi35ZWHcC2ZaBP97Dv7\n6esOkkykeceH18xpZR+dK5NIpOhqHaK4wjvrBOnrTSqZpq8nxC9/cJCltUXc/sCyKdsFh2IYjEpG\nS0NHR0dHR0dH51KOHj3Kzp07r66s5yyY9Ra0ECIf2ADkktERBinlD+dwXHOKKkGODRWTyQBJSSI1\nvaEUae6YdC4Vmix8ZrGZMlVw3DNQbNaZW8xm47Qq2vMNo8lAYUkWVYvzOH6wDUUR7Li/ZoK3KplI\n891/fgXFIPjM3+zAYjW9iSOeHemUihDc8BWf0mmtRG5fV5DgUJS8AhclldlvqdCyRDxFW5Ofiupc\nvcyvjo6Ojs5lmZWxIIR4AHgIaASWAaeA5WhibfPSWKirq0NdcnumXCqAxaxAMn3ZnIWEf2jSuXQ4\nOumc1Tb2EbqyrG9ssDpvKtcrTnTd1gq6O4Y4tr+VgtIsalYVkYinsFiN7H7mHABqWvLgP75Mjk/z\nfO24r2ZORP6uFalkmp9/9wA9ncOYLQZqVhVx812LMsZOIpGi/nAH1Ut9WG0mdj97jqZzfVQszGVp\nbRElldnT9h2PpejrDuIrdL0pyeFSSk4d66S/J0hvxzCtF/0AtHScZmHVSk4cakdRBHe+cznL1hTP\nuv9USr2mXrFUSkVRBO3Nfi6c7aOzJUBwKEZoOI7NbmLJqkJyfE4MRoXSymw82XakKkEwKexS5+rQ\nY9B1ZoM+X3TmG7P95f0K8DEp5a+EEAEp5WohxMfQDId5i6pCctxup91mhnCU8DixrUtJBmZqLGiL\nIbPFkEmW1dG5HIWlHj7x5zfzyx8c5JlfneSZX50EIDvPgb8vzNot5VQvzaehvpv+7hBdbYP85n+P\n8P5PbWSgN0RBcRaeHG1Bd7WiglORTqtXrOIlpaTuQBsXz/URDSeoWpzH0tVFvPpsAz2dmoaF023l\n+ME2jh9sY+P2KpxZVva9dJ5IKMGrz51DqpK0KskrcHHiUDsnDrXz9g+tZuG4nBMpJeFgnNN1nex7\n6QLJRJocn5MPfnoTZ493UVCSha9Qk3qJRhLEoymCQzHyi90TDIpBf4QjrzezbHXxrBPwAYYCEZ77\ndX3GQLA5zKzaUEpBaRatnQr33LeTwYEIL/72NM/8+iSRcILaTWUkYilefuoMK9eX4smxo6qSVDJN\nOi1xui04nBZAE/l7/tF6qpb42Hr7QtweG2aL4aoW6dFIgq62Ic6d7CKdkuQWOCmvzuWxnxwlHktl\nKn05XBZ8hS5WrCuhrzvIsX2tmT4Ug6BqUR5tTX4sViN3v3slpVXTG3LXEiklkVCCaCSJ0ajgybG/\nKePQ0dHR+V1nVjkLQohhKaV75HFASukVQihAt5RyXgbr79q1S+4O59Kwr4UFg1oYkbJzMc82DfLp\nzSU8sCxvyte1P/wU9Z/96oRzK77xRYrfc/eEc6+/2Mi+ly6Qm+/ko3+m7wTozJzgUIyff+8A8WiS\nvEIX7U0BsnMdfPSzW1HGGQGD/gg/+ebejF6DyWzA5jATiyRZsa6Y3HwXRpPC4ECEvALXhETvK5GI\np4jHUux/+QJnjnex4/4alq0uQghNl+TA7iaKKzyUVeXQ3hzgpSdO09sVxGAQpC8pPbx2Szm33luD\nqkp2PXGa86d7CQfjABSVeVi4LJ99L11ASsk7fn8NpVXZdLUN8txvTjE4ECav0E1+kZuCkiwOvdqE\nv1/7vlYsyqWk3MueFxpRFJFRKc/xOUkl0wSHYplzNruJ9TdXsWZzGa8938CREcVuo8mgaWZsKEVV\nJYGBCLk+JwhN3fyFx06zYl0xlYvG7geRUIKffGsv8ViS7XcvYfGKAkwmw5RhO8lkmscfOkZzYz9Z\nXi0ccbSq16UYTQp3v3sljae6OXuimxyfk3Awnin/u3R1Eeu3VaIoIuNZCg7FSMRTmWPQvDlNjVo4\nVCySpO5gK+oU5aCFgEXLCygoyaJiYS45PueE+RUJJ0gl08SjmoHT1T5E5aI8eruGCQ3HuWnnAlZt\nKKW9OUA8mqK3e5jhQJTqmnxy8p3Eo0mKyi+fM5RMpEkm0pjMBkzm6bVt1LRKU2M/zQ39NJzqycwf\nITTxyeoaH7fcs4Smhn6Cg1GKK7IRQuvflWXF4bJk+orHUhiNSubvlU6pBAbCeHMceuiVjo7ODcH1\nylmYrbFwHtgipewRQhwDPg30A/ullPMycHzXrl3y5VAOpw+1UzMQBMDz9uX88mQfH15TwIfWFE75\nuqYHf8a5f/gmt554gnQ0xqsb38PSf/ocZR9954R2e15oZP/LF1i1oXTahFUdnemIRZOkkmkcLgvN\njf14cx14sifvoJ4/3cMTDx9n9eYyetqHiYS1ZP3RSlzjqViUy5ad1VesUDXQG+LRHx9l0B8BIMtr\nYygQ5dZ7l7DmpnKef/QUJw+3A/DA76/huV+fJJVSWb+tks07FgBw8Vwf/r4wBcVZlFR4J3g6pJQE\nh2IEh2IUlGRhMCiEhmOkUuqEa4xFk+x+5hzDg1HamvyoaUleoQuHy0JfV5CPfXYrVpuJC2d6aTjV\nQ3aeg/7uIGeOd2EyayJ/Wdk2nG4r9UfaaW8KZBS1V64vYcX6Ul5/oYHmxgEMBoEQglRKJb/YjcGg\n0Nk6ptux821LScSSHHm9hUg4gcGo8IE/3EhB8ZW9EqoqOXuiixcfP4XdaeHWe5fQ1xVEKAK7w4zJ\nbMBgUHjt+Qb8fWGEIli3pYIN2ytJJVWO7m2hvzdE07k+QCvxnFvgyqiOA1Qv9ZGT56S/J0hX21Bm\nHgAsXlHAyvWl5OY7CQ3HSKclPR1DVC7Om3JOXYlwMM6zv6nPjGcUg1HBbDYQjYxJ7hSWZrF6UzkF\nJe5MNbFUMs2B3RfpaA7Q1hxAqhKjyUB+kRZOVrk4j4JiN/FYiq62IRpP9RAajhGNJBGKYOHSfApK\n3KRTKsmEVhyg6VwfNoeZ6LjrHkVRBJWL83B7rBQUZ7HnhUZi0STZuQ7SaZVAf5h0WuIrdLHp1gVU\nL82fYDTp6OjovNWYr8bC54HzUspfCyE+DHwPUIGvSym/eI3G+Ib4+te/LvsX7KT+WAcre7UfXd+7\nVvHr+l7uWpzDH28qmfJ1DV/7Dk3f/il3tL1KOhLlxQW3sfiLn6HyMx+c0O7o3mZeevIs7/3EesoW\nzEt7SWeGzPc40VQyjXGc4nginqLxlKZv4e8P094coO3CAP6+MDanmU9+bvuExVAsmuTU0Q5yfE6K\nyjx8//9/FdAWpYWlHu7/QC2P/eQorRcGKK7w0nJ+gEXLC2g53088lsLhsvD+T23Am+O4Ztfo7w9z\n/nQvtZtKMZuNk655PAO9Iaw204TdZIBXnzvHodeaqd1Yyo77tARyKSUXzvTS0TqImlZxe2wceq1J\nW2AuysNsNdJQ382QX/MG5BW4sFiNbLp1ARULp9bcmG6+XCk8LBFPce5kN4WlWeTmT0yKVtMqGuns\n+AAAIABJREFUdQda8fdF6OsOEg7GGQxEKKvKISfPwbn6biLhBFarCV+hi3XbKnFlWQkNxyZ4ReaS\n1osDnD7WyYIlPlweKzl5TlRVpeFUDwaDQqA/zPGDbURCCRCwZnO5Nm5/hJ6OYXJ8TgpK3LiybLQ3\n+4lHtRyUSymp8OJwWahZVUjZgpxJ+SlSSk4caqe7fQhXlpXqpT56OocxGQ0YzQZazw9w/kwP0Ugy\nU3QCoLw6B6PJgDfXjt1h4cArF4jHUhSWZrHz/qX4Cl3XPCl/vt9bdOYX+nzRmSnzshqSlPKfxz3+\nsRDiFcAhpTwz1wObS1RVklDGfgwUReA0GwjHp89ZSASGMHncmtiWTUtcTk2Rs7BqYxnFFdnkF7nn\nfuA6OuO4dNFsthgzCbU5Pmcm5v/cyW6e+HkdF872UlzmxWoz0tU+xIuPn6avW9vpLi7zEA0n+L0/\n2oSv0IXBqCCElqT71C9P0N0+xIbtlWy9fRH9PUGazvVRU1t0zSt+Zec62HBz5bTXPJ7xITnjufnO\nxdx850TVciEE1UvzJ4Rord1SgZQykx9w8x2LCA7HMBiUSQbIbLhSHonZYmTFuqk3KRSDMkk1PJFI\nYTJpeQw737Z0UngNcE0rMZVV5VBWdelGiIEVa8euYdOtCwj0R3j5qTMc3duCw2XBZjdx+9uXsmpj\n2aQ+E/EU9Uc6cHusWGwmnC4L3tzLG6FCCFZtKGXVhtLMudG8FYDqGh877q9BTav094SQMOV9ed3W\nCs4c72TXb8/w0Lf3kZvvpHxhLk6XhaIyD6oqcTjNnDzSQSqZRlUltRvLsFiNJBNpnG7LW6pKmY6O\njs4bZbbVkG4FmqWUTUKIAuDLgCqE+GspZfc1GeEbpLa2lmcH5YQEZ4MAl8VA8HIJzv4hTF4t9EAo\nCga7jXRksrFgMCi6oXCDcKPs5FTX+MjOdfD4Q8cALWlVVSVWq4m737OChvoeejuHWbS8YJIwnMNl\n4b2fWD/hnK/QPWFRdiMxPpFYKGJWxtD1mi9m88Tb9HwspGAwKOTmO3n3x9Yx5I/izLJeNofBbDGy\n5qbyazIWxaDgu8w9WVEEy1YXU1KRzclDbRzZ20Ld/lbSl1THE4pAoG02HT/QNnZeaN+TdErFYjVp\nhQGMCharEW+Ogy23V0/yvt0o9xad64M+X3TmG7P91fk2cOfI438b+T+KFo70trka1FyjSkiMNxYU\ngdNsJHQZz0IyMIw5eyxO2eCY2ljQ0ZlvGIwKb/tgLY/95BiD/giKENRuKmPrnQsxm40sWz378p46\nOjNBCPGWqVqU5bWx9Y5FbNpRjaIIutsHiYSTpFMqvV3DrFhXksnj6WwZJJVKoyiCoUCU/p4QiXiK\nRDxFdp4DNS2JRpI0NfRx9mQXJeVefIVu1m4tJ8v71vg8dHR0dKZjtsZCsZSyVQhhRDMayoEE0Dnn\nI5sj6urqSJfegmHc7pwiBA6Lge7h+LSvSw4OYystyBwbHTZS4cg1HavOm8uNFCeam+/i43++DVWV\n817Z+q3KjTRffpcZ/X4UlY3pmCxeMXbv92TbZ5wgPuiPcPxgG/VHOmhvDnB0XwtLVxcRSbdx39vv\nyJTaHh6M8vqL53G6LGy6dQFGk0JHcwCJZnAN+iME+sIUlXtYsGReFhrMkE6rqGmZqXIVGo4xFIhm\nihpMR2g4xolD7XS1DWIwKPT3hvDmOnB7rDQ19FNRncOW2xZid5hJpVVMJgPplEp7s59YVKsM5/ba\nUNMqJrOB7DznDXOv0+8tOvON2RoLwyMKzsuB01LKkBDCDMzrAE5VQoHHSqTQzSkMLBDgMhs4f5kw\npHQkisEx9gNhcNin1FnQ0ZmvKIrQq73o6FxHPNl2tt+1mO13LcbfH+b4gVaO7m2huf0czScMmC1G\npJQkk2kYqS3SeKoHm8NER8vglH2uWFeCJ8dOMpHWksdD8Unlb6834WCcwEAEq83Er//nMOFgHLfH\nRjKZJhpOoKoSX5GbhUvzCQ3HMFuMBIeiWO1mqmt8HN3bQlNjP3JEbyWVSpOd56CjOUBzY5r8Ijcn\nDrdTf7QDg0EhmUyTl+8ilUwTGJh6085oUiip8FK2IAeDQWF4KEZBsZvcfBe5+U5dYFBH5w0wW2Ph\nG8AhwAx8duTcFuDsXA5qLqmtraWpT6IIiFT7CHQEtTAki4HQJcaCTKeRaRXFbCIdi2OwjSU5Ghw2\n3Vi4wdF3cnRmgz5fdC5Hdq6DW++tYcPNVfR1r6OjJaCV41UUzFYjy9YUEeiP8PqLjYSDCW69dwme\nbDuxaBJfkRtvjp2XnjzDiUPtmT73v3wBgIqFOaxcX0oykUYCy8cph0spOXeim9PHO7HaTJhMWrWo\nsspsqpbk0d4UoP5oO3anhfBwnP6eIFVLfGzeoSWpSykZ8kcorvBiNBq0krOqRAgI9IdpqO/h4rm+\nTI6HyWygakkegf4IpVXZ2B1mzFYjB165yOsvNmI0KlrOlN1EIp6ibn8rFquRDdsqWb62eEJiezqt\nkk6rmM1G/H0hTh7WkswtNhNdbYOEQ3Hue/8qcvOdpNOS3s5hHC4LiXiKzpZBWi4M8OqzDcBIrtaI\n7khugZN1WyspLveQ5bWjKIJwME5zYz+xaJL8IvdlVeSvN/q9RWe+MetqSEKIR4G0lPLCyOkO4A/m\nfGRzSFpKDIpgdCNGEYIcu4loUqUvnCDPYQbg8Af+nIFXD3FX917S0TiKdcxYMDpsJIfGatpLKTn3\npW8AsPhLf6LvWujo6OjoTMLhsuBwWaYswevNcVC1ePqSt7e/fRkr1pdid5iIR1OcOtZBT+cwrRf8\nNDcOZNodfb2ZcCiR0b8YFfgzGARmi1bF6cieZhwuS0boTlEEVrsJt8fG/pcvcOT15gklZ6fDajNR\nVOZh+dpigoMxamqLMkKE49m4vYpIKIHZYkAxKAgB0XCSk0faqVqcN2UFL4NByYQuZec52X734klt\nxjO+uMiSlZpmUmg4RjKZJstjo78nRFf7EEf3tvDsIycBTYU9J89BT+fwhOv15tqxWE1keW0YjAoO\np1bGVyiCEwfbiEaS5I/ogvgKXVQt8b3hsCc1rXKxoR/jSIJ8MpHG7bVdlS6Kjs61ZNZlNaSUDZc7\nnm/U1dWhFt6MIjQjAbQE5y0VHr5/sJOXzwd4z0ofr256D9GWsdQLNRbPlEwFMNhtxDp7M8e9z7xK\n83cfBsCzfgUF9916na5I51qhx4nqzAZ9vujMlKudK0IRFJaMFNrwkqnylE6rdLYOoqYl5052ceKQ\ntgCPRZMUj4TiLFqWjwQt1j+tcupoB82N/eQVuFm9uSyTPwFwpq6TpoZ+Sio1b4LBqHD6WAe+IjfZ\nuWOK194cB948x4wXyXanedLxxu1Vs/4cZoPTPfa77Sty4ytys3J9Ce3NAQb9EZob+gmH4ixcms+6\nrRU4XBZOHmmnt3OYRDxFT8cwiUSKWDTJodeaAM3oMpmNnD3RlenbaFLIK3Bhc5jJy3fhybWjpiVO\nl4W8QhcGg0JP5zDptEpJhReb3YyaVmlvCTAUiNLe5Ke9OZDRdhlPV38DtSvXsXJDKZ5sO4pBE3b0\n5NgvmwcyG0bza0oqvJRWZYOEztZB7E7zjKrfqWmVQ3ua8WTbyc5zMOSPkEqpVC3Oo6dzmFNHOzAY\nFYrLvHhybNgcZgwGzctkd5gxGhU6WgYxmg1k5zomlYO+lHAwTuvFAcwWIy63lbYmP+FQPKNKPxyI\n4smxY7Obp+1D5+qZfzX4rgGqlBjEmGfBIKDIbaEq28axziDvrHZNMBRkOo0aT2AY51kw53gZePUQ\nnY8+T8H9O+h+8mVMXjfJwDDh8y3X+5J0dHR0dH5HMRgUSkfCZsqrc7jl3iWTSuxe2n7l+lJWri+d\n8vma2iJqaosmnBuf5P1WRwhBaWU2pZXZE/RBRtl0y4JJ50LDMVov+FFVlfLqXGwOMwM9QbLznLQ1\n+blwppfm8/3EoymaG/pR1ekFbhVFqxI2FIhmwresNlNGTXw0QdxqM9HTMczuVzpJxFO88NipCf1Y\nbSZsDhMWqwlXlhWHy4I3x64ZRYWuSfofmnJ5hHgsyUBviIHeEMGhGKmUSlODljNy6NWmSeMtqfRq\nQoY5dhYtKyAU1PJOhBAMDkQYCkRobhxgoDc06bWjmC2aNsz4ssOj2OwmhBATFOitNtOId8dI1RIf\nQgjOn+6hryuIlJJoNJnJ8xnPwd1j4zcaFaqX5mNzmCgu91K1OG+SuKPO1XHDf4q1tbWc6tS8CqOe\nhdH/811muofjpEITE6Y6H3kOYIJnwbmkilQwzIk//hLB+ka6Hn2Bwgduo+eZ3aSC4Tkf97E/+Bvi\nvQNs+u135rxvnanRd4l1ZoM+X3RmyrWeK5czFHSuDqfbytLVEw2o/GLNy1O1OG9C+FgqpRIajqGq\nkuFAFH9/GDWtkpvvwmgycKauE39fmKoleRSWeMjxOfHmTu0lqFqcx+YdC5BS0tcVzCyow8E47c0B\n4rEksYi2+G+9MEA8lsq81mQ2aIZkVTZ5BS7O1HVOSAg3GBUURWA0KqzbWpHxuETDCVJJlcLSLHo6\nhjld14nJZKDtop9j+1qn/Hx8hS7ufd9K1LQkNBwjt8BFIpZiaDCK021l8fICjCaFi2f7iMdTmapZ\niiI4d7ILk9lIzarCjEET6A8zFIgy0BumuVHT+XVlWSmtysZsMWKzm1i0ooB0SmXQH8FX6CbLa6P1\nop8hfwSjUaG3K8jpuk5SyTTH9rViMAiqFvtYf3MlhSVZVxTM1Jme34k7jColJoOYEIYEkG0zcron\nPClx+eSffQUAZZyx4KoZc502feunAORsW8/Aa4fn3FiQUtLz5MsAxDp7sRbN79J5OteGgXCSRFql\n0H1lNeFIIk1vOEHFFLHDOjo6OjrXDqNRyeQZZOc6JuWnlFR4p3rZZRFCTBIXXLZmskbOUCBCd/sw\nrRcGUFVJIp7i/OkeGk/1kJvv5I53LMNoMlBQkoUry5oxUEaraV0qIFi5KI9Nt2qeluHBKN3tQygG\nhSF/BLPVSEFxFtl5DhRFzChXc0HN5PXLdAr2oK1/wsE4Umr5PlNV/SouH/s8qy/pf+f9NSAEna2D\nNJ7qpv5IB42ne7A7zdTUFlFWlU0ynqapUcsVWbS8gNKqbBrquxFCUFzumRDKpqPxho0FIcR9QI+U\n8tAcjGfOqaurQ83dgiKUTBjS6NTz2kwMx1LEh6de7I8PQ3LVjLkps7esofTD7yD/7pu5+I0fz7mx\nEO/pzzw+87f/Tu0PvopQboz60fOZ+RSDHk+pfPDhelQJ/3xPNbWF05f+O9kd4j/3tNE6GOODqwu4\nfWE2+1uHqPE5qPE5pnyNzhtnPs0XnfmNPld0ZsNs5kuW106W1z4hbGygN0Qqmc54Qq4Wt8c2K1X7\nuUAI8YYW68qIMVRS4aWkwsvmHdVcPNdHY30Px/a2cGRPM6CFQqXTkuMH2zAYBOmRylkGg2DhsgLy\nCl2YzQYURZCT76KgJItIKI6iCM6f7sVqN7FgiS+jL3Kjc1XGghDih8B24DjwY2AZWknVeYkqGUlw\nHj3WJkW23YQEhvzBKV9nsI8ZCyaPm8J33YHv9i0UPnB75rzR5cgYCzKdRk2mJhgZM2XUm5B7y0aG\nT2g543k7N9Pz9G78e4+Rs3XtrPvUmd8MRJJ4rMaMp2uU+u4QX32pmdEQ2M8/fZ6tFVl8dmsZDrMh\n075jKM7xriDf2tuOIqDYbeGnx7r56bHuTF/vXJ7HH22afhdHR0dHR+fGIsfnfLOHMG+w2kwsrS1i\naW0RsWgSf18IKaGw1IOqSk4d7cDfF6KkIhtnlpX6I+001PdMSGa/HL4iN5t3LKCgOAu7w8yFs730\ndQeJRpJIVeL2WMkrdJOb78SVZX3LVs68Ws/CU1LKjwshNgMfAabPcnmTqa2t5XDLaIKz9kcaXYRl\n27XLHwpMbSwolyz6V33rS5PaGJ0OUiHNWDj5Z1+l85FnubPr9VlPiMFDJ6n75N8C4Fq+EGEwsPzf\nv8Ara99B/0v7dWPhOnA9d/4eP9XHg/vbKcmysrLQycnuEJ/eXEKx28Lfv3ARl8XIP9xehcdmZH/L\nEL880cOe5pPYTAqf3lzC4jw7f/Sbs5m5/I23L6LCa+OTvz5DdzDBF3dWcrh9mN/U97GtwgMCrEaF\ntAqL8uzEUyrtQzGaAzG2V3kxXmKwtA3G+PqrWqzqX24vozhr7tyyaVXS0B9hcZ6d+u4wu877qS1y\nMRBJYjEI7l86fSnJN0oipWKeI5VXfadYZ6boc0VnNujz5dqglf0dC2FSFMGqDROT/gtLsth5/1IS\n8VSmtG7bRT9dbYPkFrhASpxuK6oq6e/RciQef+jYpPeyWI0oiiAaSU54P5PZwJKVheQXuzUPUEpl\naW0ReQWuaZOxpZREQgkC/WGy85yTqoxdD67WWEgBSCn3AfvmbjjXBlVqSTWXeha8I6XjgtMYC+MT\nnKfD6LITbe8BoPORZwFI9Aew5M1O4CXeO1YzO1jfSFZtDRZfDtmbaunbtRf7n36Ci/4o26tmH/uo\n8+bSNRynO5SgwGXmcNswT58b4MJAlDKPFVVKnjyjhZ19/unzmdf8493VLMrVYmBrfA62Vnp48kw/\nZ3vDmUW822JgQY4dIWBRrh0hBN9+YDEX/TFWFjpZV+LicPswf/FUI+MLdSzOs9MXTuCPaIlxbYMx\nPrpOS+Tb0zTIDw510BdKarGiUvKxX51hYa6N319TyOoiF5YZLrZ7Qwmk1AoJhBNp4ikVoyL45t42\nXrk4iMtiIBjXbsbPnBub/02BGHcuymZx3sQQqpQqafZHWZBjm7Uxnkyr/OvuFl65OMj2Sg9f2FHx\nlt3h0dHR0dG5diiKwGozZcoLL11dNCnZHWDhsnzWb6uk5fwAoWCcIX8ECWy5bWGmvLC/L0SgP8JQ\nIEo4GGcoEKH+aAfHD7ZpbcZVjHJlWVmyspDsPAftzX4GB6KYzAoDvWGCQzFAC5/KLXBhtZpwe61k\nFV6fz+RqjYX1QoiPAA8Bu6SUQ3M4pjmlrq6OdNZmDON0FkYXTl6bdvmhwTACWP7vX6D+/34t89qZ\nhBOND0MaJXTu4qyNhWRA+wgr/8+HaPrmQ5hzNaOgf8VKxLd/yMmlt/G/f/K33PRXd2CaozrLOhO5\nVnHFX3mpicb+sST6Sq+Ve5bk8MebSjAbBGkJF/1RjrQPA9piftRQGGVhrp3/u62MtCp55GQve5oH\n+fTmEmp8DqSUmYWv02JkZaHmgraZDPzjXdV8/2AH8bS2ULebDBzvClHhtfLRtdm82OjnZ3U9XPRH\nWZbv5KfHurEYFW5Z4OWdy/MIRFN84dkLNPZH+bvnL1KdY+OLOyszSdfRZJpgPM1QLMU397aRUiUb\nS7NQBPzkaDeKgHyXhc7heOZaBJDvNFPjs7O8wMm2Cg8/q+tmTbGbZ8718+SZfp49N8DH1xdx0R9l\nTZGLc30RXrrgJxhPs7HUzZYKD7ctzJ7kEQEt36M5EKU5EKM/nGQ4luJcX4TTvWEqvVZ2Nw3S8MvT\nfHZrGcvyHVz0RznTG6ZtKM6GUjflXisOkwG39fK3Rz0OXWem6HNFZzbc6PNFSklSlZhH1jL+SJK0\nlPSHkxzvCmI3GVjic7DwKjaGrjdGk2HKJO5RsvOcZOdNDAtLJtJEwnGcbiuJeIpzJ7qJx5J0tQ1x\n+PVmpCoxWwz4Ct3EoinyCl2s21qBK8vKsf2tBPrDRMIJFEVw89sniz1eC67WWOgEXgJuBz4vhAhI\nKe+6mo6EEApwGGiXUr7tkue2A48DF0dO/UZK+ZWR5+4C/gNQgP+WUv7zdO+hqnJC6dRRz4LdpCWm\nJEMRzJBZoI+izMiz4MyEIZmyPST9g4TONpGzdd0VXzuehF8zFso/+V4CB45T/ZeaKPYvrCW8H1BU\nlZrjB+kavoUyr56pP1/pCyf4rz1tdIcSuMwGWgZjBONplvoc3LU4h8psa8YLMIpxxDNwqYEwFQZF\n8L5V+bxvVX7m3OVupmVeK1++c3IN8VFuW5jNz4518+ipPva3DlOdY+PLdywgxzFWr/vpj9fyWtMg\nRzuGea1pkI/88jSKAJ/TTCotGYqnUNCME6/NyEMjORMbS90oQpBSJfcsziGSTHO4PcifbCmZ5DX4\nzE2aK3hNsYsXGv384GAH3zvQAcCLjX4AqrKtFLosNPZHONA2zPcPdvCB2gLevcJHbyjB7osB8l1m\nvrO/g/5wckL/RW4zn91ayq0LvHzgZ/V0BRN8/pnzKIIJXpdRL4/LYuAH767JeB91dHR0dN4YybTK\nM+cG+OWJHvpCSXZUeynOsvJYfS/D8cnq4QYBS3wOqnNsKEIQHqe47bYaiSVV3FYD71mZj2NconFD\nf4T67hArC5y0DcWwGBWK3RbKPPMjZ8BkNpBl1n7vbXYztZvKMs+Fg3GSyTTuLGsmWXs8i5Zriexq\nWkUogmPHJodAXQuu1ljYD+RJKf8aQAjxRtLl/ww4DUwnGfjqFEaEAnwT2IlmuBwSQjwupTx76Ytr\na2t57byW3CwyYUja/6Oxy2pYq0NsucRYmI1nQUqZqeHb+cizFL//HozOmVeiSfqHMNisWPNz2fTE\ndzPn48XFnK9ZRfWZ45RebKB1KMZAJIkiYFWRa8b961yZ6XZypJSokkmJyP5IkiZ/lLUlY1P3x0e6\nONCmeQhcFgPL8h0IBH91S/mEm9l8wagIPry2kLsW53CmN8yWCs+k3XqjIrh1gZdbF3j50OpCdl8M\nEE6kOdoZJJZU6Y8kqfHZ+dJtVXjtJl69GKA3lOCdK3wZA32Uj17BhrYYFe6ryeXmSg8H2oaoyrbx\npReaeMfyPN65XNu9kVLyxJl+HtzXzvdHDIpfHO9haKTeuM9p4rNbS8m2m4gm09xS5Z3wA/G9d9WQ\nTEtO9YToGIqTZTOy1Oeg1GPlRFeIvnCCB/e1876f1vPJDUUjngqt0lSpx5r5fG60nb+2Qc3NbTEq\n+CNJFuXZSauSUCKNx2qc9kc2FE9xqieM126iOsdGMJ6mczjOolz7pO/M7ypvpbmiSkkyLWccbjhf\niSbThBJp8hxvPUXft9J8mY7WQIznGrTw0qQqiSTS7GsdIhhPs6LAyeoiF682DRJNBvA5TeyozmZZ\nvoPVRS7iaZUj7UFaB2Oc7A7x0oUA8ZSK22okmZZEkmmS6bFdntebh1iYZ+dw2zBpKTPhrZeyptjF\nJ9YXYTUqZNtN8/I32eGaWYGcqQyJa8lVGQtSyqOXHE/WK58BQogS4B7gq8CfT9dsinMbgEYpZctI\nPw8DbwcmGQswkrMgBIZLPAtmg3acDkdQbBYMromLe4NtJsaCHZlMoUbjJPxDWIt8DNWdofOR5yj7\n6Duv+PpREv4hTNljZc5iKZWP/vIU/miK7H/7e0oef4T09x7mYk+Qh05qu5+/+L3l9EWSpFVJpYhz\n9IOfY8V/fAHX0uoZv6/OZFQpOdA6jD+aJBhP8XyDH4Mi+Jd7qvFYjTx2qo8Xz/tpCcRIpCXvXuHj\nzkXZ9IQSHGofZlulh9uqs1lZ6JyXN6Op8DnN+GaQNJXvMvPeEa/Gx0bOpVSJQYx5OG6eg7wat9XI\n7QtzAPjJ+5dNeE4IwduW5rGzOps/fvQs3zvQQWmWhc/fUs5gNMXqYhc59uk9AqPXWZw1+fu9uVz7\nDuY5zPzqZA/fPzim7P5q0yC1RU4+f0vFZft/q5FIqTx1tp8H93dMOO+2GIinJfGUSr7TzKpCJ+1D\ncdJSsrM6G6fZQK7DxH/uaaNjJMysJEsLOVOlpmNz+6Icfq82H5vprfE9+F0gpUoOtw8zGE3ROhhD\nlZIsq5GeUILD7cMMx9LEUipLfQ42lLopcluo8Tk41x/m4kCUVUUuyjxWcuwmkmmVlCon/H2D8RRt\ng3HO9mked0UIPFYjWyqy3lAIbXpk3NGkitWkkGU1EoqnaeiP0ByI0jEUpyeUIMduIppUtZwpoMZn\nZ1NZFjdXeshzmImNLDp1rp5L7/mjDEaTxFPaRsy/vdZKMi0xGgQmRSCBTWVZ7Kz2sr7EjRCCP91S\nioRMONJ47lqcM+F4fLhtMq1mxnGyO8R39newt3mQ9aVuPFYjTouRmys9NPZHKMmy0hdO0DYY49FT\nfXzmsXMAmBRBqcfCxrIsPFYjXpuJLKuRfJeZeEqlwmslklQZjKbIc5gwKGLC5oeUku5gAqtRoTec\nwG4ykGU14o8mOd8fpXUwhtuilV71WI34nGaqc+2YFMELjX72tQxxti/M8gInqipZVuBkeb6DQDTF\nia4gOXYTNrOB7mCCIpeZBbn2NzUsS0g5vUT5NX9zIX6FZihkAX8xTRjSr4F2oAP4SynlaSHEu4A7\npZSfGmn3IWCDlPJPL32Pr3/96/Jzn/vcpPf2+7XQhvt+VMdHdz9K9uHDbH7uhxSvqJlyrKPtLyU7\ne+rchJfe/Rlqv/flGbd/4Z6PM9zRy5LHv09aSr6yq5mL/iiH/2rnlO3X/cuuCcdfTV9k4O+/zu8l\nprSXZj3+37X2HT19fOo/f0X1qg04LQaea9BeP93nf9c3X2VtiYtANMXBEU/C5drPt+u9UdpfGIjw\n8oUAH6gtwGE2zGn/qpTsaR7EalSIpyQ/r+vm4U/cNCf9d/f1T/kD+WZ9nuv+ZRdFbgu/v6aAUDxN\nSpW0Dsb48ttrp20/isti4LNby+gJxvnULVPfP1893YrXZppgoMVTKoW+qeNt/X4/aVVO8ky82fPt\natuPxqBP1/5LTxzHYVKozrVjMSqsLHBiv8x8frquiQf3tRNNqlqOU56druE4//TONVO2/+CP9tEX\nSmiFBsbtyE53v/r3F07x8gU/ncOJCeena//gy2dYnu/gFyd62N965fvhl588QVqV7GsdIttu5P6a\nPJblO1hVNTmJFOBkUyf/e6SLVy4Ozmg87/ze69xUnpVZTJ7ri1y2/X++eIpgPE2Nz8HOmQtTAAAg\nAElEQVSCHBulWRZiKZXi/Kkrs72R+ZBIqRmDZbr2v/3tb9m6daum4hxO4rEZMRuUy/bfF05gNmhG\n1JXGc6alm3yXmbQqR6IuxBXHL6XEH0nxwvkBfnasB6MieOWzt0zZft2/7GJxnp1/uL2K7JGNFSkl\nOTk5U7a/Xt/H4ViKVy4GCMbTtA7G+Nd3Tf19Wfcvu/BYjQyOV8dWBPs+t2Pa9peiCDj4l9Ov3+wm\nhXKvlUA0hZRoxvoV1nuj+Yz+SBKnxcBt1dmkuxvZuXPnNbcg3jTzWghxL5qYW50Q4ham9iAcAcqk\nlBEhxN3AY8Ci2bzP7t27L/t8pOk4Z9svsM1hm1H1oz179gBXdhP69x5FSsnrr78+s/bdA1xIGPnr\nf/wZAO4FU/9Ij1LusdLX1kPx3sc55ytjgCs7dwaP1HOo4Qy20sIZuzlner2j/NcvniaUSPOF37//\nmvQ/m/ZSSn725C46h+Os3rCZHdXT73j/z+Euzg9E6dqzh1AijXtBLe9e4ePwNO0f+sAyFCF47bXX\nUBngMOV8bF3htO2vZvx6+yvTdeYoSwCHebKy6RvtXxECpeMUCWDb1q1sq/Tw8Cembvunj5/ji7dV\nUn94P4+c7GU4t4ZtlZ5p+/7Er87wd7dV0nP2KKoqsVetovIy6tvf3tfOub4wN5vaEULw9jtuvWyI\nz0A4yUV/lIa6g7gsBm65edtld1Pfu9LHnYtyaKk/jIWxz2fydofGzz+wnHAyzW+ff5kyj5VtlSsB\n+NQ07f/8yUYE8J7sXtqH4tQp5cRS6rTjed9PTxJKpPEOnOXeJbnctGXLhLCDqYgm07y+53VSqspd\nO29hMJq8bHu4fvP5SjzfMIAqwd+oxR8XLFnD4rzpc5j+7vmLOMwGCocaONES50D+Uuym6XftrUaF\njWVZdJ05Qmsgxgfvv41Ct4U7/mrq9h9ZW8iH1xSw65XX6IskcFSuIs9h5t5p+v/vQ5oHLtFygo1l\nWdyzczsrCpxUT9P/U2f7UaX8f+zdd3ib1fXA8e/VtGVL3tuOR2JnOMPZBDIgYYSUMPJjbyi7UEop\no0ALtMy2lA7K3ruUvQkkQBIgO87eiUe899TW/f3x2vKSE4s4RUnv53l4ovFauhI38Xvee865pLXs\npKTVxV9r8wMf2OGy/2wFYLIoZkRCBOn5k2h3e7mon+OfPVMLWpctW8aoOBg2ezLfFTX1++/zjr++\nSPnwUbzcrp1UZo+ZTH17//Nn4Y466trdFG9aTbhBx6knHkfWfmoJb/10Jzmx4axZ/j276+0Yhozl\n7LH9F8U+uXwfT5RuomrbWlpdXrJGTyLJ2v/K73mvb6Ku3U3rnkKGRIWRWzCF2v2M/9r3tpEWZWbd\nyh8YlxLJBfOP92deBHLbpzspLG+laXchACceN5NYi5Fv+jn+nhOymZoRxQ/fd53/DOSK+KH++7hh\n9XJigVM7jv9zP8ddc1Qa22vacRZtINKsJ23URMqbnf22/rxpxhA8Xh+b1iwn0qxn3pxjyYoJJ+GW\nwMffNTuLGdnRPc4Pq1pcjOzn78tLZ49iZWkzr3z0JUu2bsbsteP0SJ6sq+C3553InDmBg4zBdMCV\nBSHE9VLKxzpuD5NS7trvDwz0jYV4ALgQrQ1rOGBFK2C+eD8/sxeYiBYw3NNZVC2EuB2QgYqcFy1a\nJP+01cjUIVHcNGNI76c57/VNLHjtKVIdzRz1ydN8mXWc/7mTypYi9PtfPnc3t7Io70T//fHPP4h9\nXyXbfv93Zm/5DFNs/zsotheXs2TqmdrtxCTKUjLYcs117K7TTvxPGRHP9OwoJqTZcDU0s3jkXIbf\nfT2ZV57N8l8+SMPHi9C7XDTGxhMREYaxdB9AwPf1OV0szDwWgLmV3/cZi6u2gbWX/5bhv/sFMZPH\n7Pcz78+Jz2q/7P5xah4j/gu7Bxc12PnTN8XMzIlmXIrVv2OxlJLXCqt4eU3XxioZUWacXh8Pnzys\nx74BrU4P572xmVnZ0fxqxhA+317HsLjwHuOvbnXx4upyrjkqvc9Jl5SS8mZXwLQW5cizp87OpqpW\nlu5tpLrVRV6ChRUlzYxKisCkFywvaUYA3f9lNeoE+cnaEnNJgwNDx5L2zOxodtW1s6feQazFwIQ0\nG+0uL98Xaw0P4ixG6gL80rcYdZxfkOxvb7twZz1FDQ5iwg20urwBT6zz4i3srNVa+1mMOsalWrGZ\n9cwZFkvBIax/KmtyUNHi4qkVZRQ3OPyP58aHM3toLMuKGtle046no5hsfGokyVYzTQ4Pq/c14+r2\nWaxmPeeMTWJcaiRFDQ48PonL4+Pr3Q1s67h6rBNaGllVqwujXjA1I4pfTc/AqBc8taKMNftaqLe7\niQ03Eh9h5ILxyYxLiQyYIlPb5iI63Biw61ZNm4tVpc00OTws3tVASaODglQr2bFhzMqJIT3KzPfF\nTawubSY63EiYUcdHW2pod2tBksWo4/jcWEYnRTIhzYpeJ6huddHo8PDZtlo2V7UxIzuaFKuZ4gYH\nbW5txWfakCjiIoxkx4QR3VGA7/L40OlEj3Ha3Vq74uhDXKQvpeSz7XWsK2vhhmMy9huUuhub8bY7\nIDEel8dHhM9Dy/Y97N2wm+q0IUTnZQFanVRFi4vyZiduryQnNozRyZFkRA+suYeUkuaNO3DV1BM1\nbgTNW3ZRu3g5VZ99i6OsCukJkNMuBIbUJJx6Aw1DhxE2KpfIU09k14rNNDe18030EISUmJzaHHaG\nWxBeL/HV5UQ2NzFMOPHOmcnmehdVLU7Q6YizGEl0tZG8fQvO4jLCDDpiIs1Ebd4MNbXsGD2BVlsU\nTnM4bTOORvgkHp2OtPXrSC3Zg9WsxzxvDttjUqhodjI53cb07Ggq9lRQ/9ZHeNZtwmc04o6PJ+zS\nc6i3xrCjtp0Wp4eECBNmgw5bmIFt1W2k2sycNTaRCJOex3/Yh9cH7W6vf9UlEKtZj9cnMep1HJsT\nQ6rNRH5SJHn7CWSVQ6tzxdXu9vL17gaS20v+KysLAwkWmqSUUR23m6WU/RUi//hBaOlGgdKQkqSU\nVR23pwBvSSmzhBB6YDtagXMFsBI4T0q5tfdrL1q0SD64xciMrGh+OT2j99Nc+u9NLLjnNobMnMC4\nJ//AFynH+J8LdFIdyMozb6Bl0w5ybryEzCvOpvqLpRRecSdHL3oJW35uj2Pr292sLG1mYroV045d\n/HCy1vXIq9fjOv0UHFdewgurK5iaYevTxWbRqHkknTyDlNNPYNVZWsaVbexwmjdoOXjhGSnYSys4\n+ssXsOQMYcX8q4mZOo7Y6RNBSgqvuBOA4zZ+3Ke1646HnmLP317CnBzPsWvfR+h0FD3zb6LGjSQ8\nMxWDJRyDdf8n/20uL2e8vAGAE3JjuWVW5oC+v4Pxi/e39WhL+u5FY4g0G3hnYzVPrSgj2WoiPymC\nRbsaAO2kzRqm5/JJqUxMtxFnMfqPffz04QwbQEciRent2ZVlvLWhGoDrj05n/sh4yptdrCxtYmZ2\nDBaTzp/XLaWkrt3NXV/sobjBji3MwOyhMWyvaae6zYXd7WNSuo3xqVZOyI1lW00bDrePZKuZ19ZV\nMDwhgu+KGyks79oLMzHSyNy8OIobHNjCDCRFmjDqBdHhRhxuL9tq2tlQ0cqkdBuzcqIZnmD5r7dg\nbnF6+GBzDRUtLs4em0iKzexPxappc1HS4MBqNvQ4EWlxeviuqAkpJQ6Pj7c2VAcMngDm5sURbtLh\n9krq2tyMTLLQ0O7hwy01eKXWWUUC07OiSYgwUtHiYk+9ncoWLdUm1mIg3mIiLsJIfbub3XV2PD6J\nxahjeIKFqDADEqhv97Ctug1vR+MDgBSriZGJEXxX1IhX4g98AKLDDLi8PtrdPsalRHLdtHSsZj3x\nh2HhbTCklLTtKqZ12x6aCrfitTsp/89neFraiBo/Cp/LTcvW3eDrWmFKPHkmKacdT/Kps2lcuxlP\ncxuOcu3k3pqfCwJ0JhO2MXlIrxdHWTXCoMecGAcCfHYnbbtL2HjjfbRu39tzQDod0RPziczLIm7m\nFOwlZbgbW4ibNYWI7HT2/PMVWncUIXQ6mtZvw9vWjjAakG4tFcU8Yiju2gZ8tdrqg94aic/pRLq6\n5mO7LYoweztCSmwT8jEIaFixvs93EzlyKG6zGceGbYiOzy/0ei2/Qgj/ewq9Hun1kn7+fIyxUdR9\nuxJTQhy1Xy8HKbGNyQOho3XHHqTXh3XkUExx0SB0RI0fSfZ152MvLqepcBu6MG2+OStriRg2hITj\nj0bodJQ1ObC7tflZUVRJVFM9XpOJdp8gu76Sli+Xgk5H5iVnYM0fhjHq8G6sIqUEnw+h12PfV4mn\npQ1DpAVTXAx6y+B0mvS0tPlb6ptTEoKuM/C5PdhLK6j7diVNhVuREsLTEnFU1hKRk0HkiBxMcTE0\nr99K7bjskElD2iOEeATYDBiFEJcHOkhK+fxgDEgIcbX2cvJp4EwhxLWAG7AD53S8l1cIcT2wkK7W\nqX0CBdD2WfAZJ6Hr5/dibF0NpoYGYo+Z2ON/6EADBYBJbzwKPh86s/aXMSxFy3N0lFf3CRbeXF/F\n+5trmDYkiuv1XdtTuE1mRt1wAe027YQ8ULGpddRQmjfuRGfUrhQZbJGM+ftdfHecthhbO3IUEaUV\n7Fq/B/OrH9KyZRctW3ZR8sI7pF/YFYfte/0jht54if++p7WN0hffBbR/SNZf/XvcTS3ULVnV9T0d\nM4Ep7zy23+/ho6012rHhBr7cWc/o5EhOHh44R3EwVLW42FlrZ2qGjS3VbbQ4vfz87a3cflwWn2yr\nZXiChX+cmocQgvMLktlV105WTDi3fbqLvywpYWqGjfFpVp5aUUZ+UgSV29Yy7AjoQqH8d3TvhX72\n2CQEMCsnxh9wpkWZOSOqb6qBEIL4CBNPnDEcT8dVu/3JT+rq0X3rsVkA/GxkPGv2NRMdbsBm1orn\n9peWdPKIID/cIWA1G7hwQuAdhBIiTAG71ljNhh6FjqflJ1Dd6mJLVRvp0WHoAB9awWP376m7GdnR\nrCptprLVxSkj4xmT3HWcw+Pjg801tDo9NDo81LS52V7TRnSYgRNyY/0bB35f3ESYQYexoymGQS/I\niQ7n6qlppEWZ/S12O4OatzdWs7W6jfMKkslPiuC7ZcuYOu2Yg9o9XEqJ9HrRGUK/ONfV0Mz6a39P\n3Tcr/Y/pzCbiZx9F5PBsahZ+h8/lYuhNl2LLzyV8SAqVHy6m7M1PqP5sCVvueAR3ff/bN+nMJnxO\nV7/3jTE2Rv/1txiirNhLK4jMyyZ60miMtsBzBCD/Tz1zQOq/X0fF+18ROTwb6fNS9sYnRE0cRfSk\n0XgdLuylleDzET15DJF5WTir6ih/5wvC05PRmU3Ufr0cr8nIsFuvJGbKGCKGZaIzmZBer/9inaet\nHUdZNe1FZTSu3YTP6Qafjw3NNZz5p7vxttvZ8cCTlL70HgDW0bm0bt9DxkWnkXzqbP95S3vRPvb8\n4xVadxXjqtUujO1+5Hn2/POVHsFMd6a4aAy2SHxOF666RowxNmRVHY3dLiDvAsyJcfjcbqo++AqA\nyBE5ICWm+BgihmUSnpFCeHoyrdv3ULdsjRbseL049lVhjLFhjLZhio/BXd9ERG4m7Xv3EZ6RzIh7\nf4kuzEzjyo1ETxpN04ZtNG/YgSnWRtyMyTgqqhEGA5F5WYiOEzjp9eKsqsNeVkVT4RZat+0BCV67\ng9jpE2nesB1DpAVDpIWar1fgKKvCa3diHZmDbcxwvO0OqhcuxdtmJ3J4Nk3rtiK92iqTLsxEWHIC\nXrsTY7QV29gRJJ40nfjjpmIvqdB2bh6eTdXnS2nduhtTXDQ+jwfp8mh/en0Ig576paupW9qV8GYd\nNYyYaQVItwfp8WIvq8QYZcPd0ETczElk/vxs9JYw6patZsvtf0Ho9bTtKfUHjKaEWIRBj7OiJuC+\nXomf7v+8bLAMZGUhD7gVyASOA5YGOExKKQNXfvzEHnnkEfmRYRIn5MZy3bT0Ps8/fNtzjHvpOaYv\neZ3IvCw+Tz6amKMKmPr+4z/6PR3l1Xwz4XRG/elWhlx8eo/nrnl3K3vqHWREmfkDxWy8QcsK3njc\nifzm9bsB+HJnPdOzorH06qSz/Y//ouipNzHYrMQeNY7xzz8IwOfJWtHlR+f+nPlvPuc/3jJ0CO27\ntd1+DVFWwtOSsGSnU/3FUsb883dYMtMRAmqXrmbnA08y9aOn2PCLe7GXlBNI7h3XIIQg54auTNHy\nZieRJj31dS1c++FOYqItXDUljQe+LgLgg0vGDnonFJfXx9sbqllZ2syW6jaeO3MkGdFhLNnbwH2L\nivzHXToxhfPHJ/f5+YpmJ7/8cIe/1abFqOOuOdk4ijYcES3rlP+OUNo4yVXXyI77nyBu5mTa95bS\nvHEHjooanNV1jLj7BuylFfhcLjyt7fg8HuJnTCb+uKkHTLM8ErTuKKJm8Q9ETxyN9HoJS0nEkhm4\nkHagfFJ2XAQe2AW97nPFVduAp62dsLSkPif+jqpamjdsJ6pgpP+E0lXXiKuhiT1/e4mKD77COiIH\ne1kVxigr1lHDiJ02npQFJ6IPMwe8MupzuqhbuhpPaxuuuibC0hKpX7aG8ne/RGc24m5owjoqlyGX\nLsA6aig6oxFdmLnHd+RqaKbhh3VY84dhyexZH+RpbcPT2o7X7qT++7VUvLOQlm278bS0kXvrFVgy\n04mfPRW9Jdx/0tcf6fNR+uqH1CxcRtysyegMBsIz07Bkp9O+dx/S7cZV30TrjiKt1XhaIkhJ67Y9\nGGxWDFYLxigbiSdN166wH6a6zxcpJc3rt6G3hBPZkaY1EA2rN1L+1mdYcjJIOnmmlv4lBGHJ8dR8\nvZzqL5aBlOjDzBhskbTuLCJ6fD5RE0bhrm/C63RhiraRcOIxSLeH6oXLsJdWUPXpt/icLnRmM/bS\nctwNHQXtOh3Rk0YjhEAY9OjMZnxOJz6ni8a1WwjPSMZRXk1YSiL2knIM1gh0YWZcNfXoLeF42wPX\nXRqsEVhyMkBKWrbs6pFCJgx6DLZIpMeLp7kVfaQFn8uNdLmxjRtBZG4WOpNRu3C6dTdCryf+2CkI\nkxFnZQ3RE0ZrwaDXS+v2vThr6hEGA+76RprWbcFV17OgvndQGogpPoaMi07HnBiLz+Wm6rNvaVq3\nFZ3ZhM5oICw1EXdzK4YICy1bdmFKiMU2Ope6pasx2KzETBmDJSudiKEZxE6fhCUrDSEEnjY7eksY\njas3AeCsqsU2Opdt9dWhkYbU42AhFkkpD30lxSBatGiRvHejnnnD47j6qL7Bwt9/+U+Gv/UGc7Z/\ngTHKiruxGX14mH+V4MfweTwsTJ8JwIzv3iRiqFYr0ezwcNarG/25zH9s2kjDn59k/bRjsd34c649\ntv/NswAqP1pM4ZV3ATD2sd+Teqa2D15nsPDUbQ9w9cN3AGC//irOuOtSPG12Fo04Cen2kDRvFqP/\ndidrLvwNjSs39Hjt8IwUZq16h7a9+3BW1lD58dfYSypImjeLTTc9gD7CgrdjPwrtSslYrFPGcv6/\ntxJu0HHunb+iIiGFUxc9R2KkiYU76vjLkhKumJLKWWMSB7Xd13Oryvn3+iqAPulai3bV89GWWkwG\nwW9mZvbbDvSH4ibu/nIPsRYDr507WvWDVw5rG391P2VvfuK/L4wGTLHReO0OPM2tPQ8WWp1D3IxJ\nZF17PuHpyf6TkJrFyyl66g3Szv0ZSXNnDah9dDA8rW00b9xBxNAhtO7Yi72kAkdFDcYoK+bEOCzZ\naYSlJrH38ddw1jSAlPhcLhKOP5qkecdiiDjwlj7edgfl73yOp6Wdtr2llL3xcZ8c9ZQzTiDvjmsw\nJyegMxpoXLsZV20D8cdOpWndFrx2B3EzJvmDKZ/bg3R7+k1TkD4fzZt20rx+K+1FZUiPl7Rz5vnb\nWLubWih54R0q3v2S1h1aekzkiByM0TZat+9B6PWEZ6TQvGE70uvFYIsk5YwTkB4P+177yP8+8bOn\nId1uzEnxVH22RDvZ77gCLwx6IkfkYIiMwGt3oDMZiZ6QT93S1bRs6VVqKASJc2cgXW4s2enULF5O\n+57SHocYrBFEjsjBXlqBs7LW/7ht7AiS5x9H5Igc2nYVs+uR5/G2duW9R+RmYs3PJeuqc4iesP/C\nZeXw525uxbGvElNCbJ/05k7S69VSqnw+hE5H45pN7HvzEzwtbZhio3FW1RIzrYDkecfiqKyl5svv\nMKdofzeb12+jbW8pntZ2YqeNJ3xIKuFpSYQPSSFimJbq7G1tx76vUrvv8+GorCV8SEqP8w5fx5V6\nnXFgK3PS66Vm8XJaNu/EGGVFGA20bNlNzNRxJMw+Cq/Dic5oQBgNWraHTkDH6kLvizDS6wWdrs95\nUP3yQoqfeYu2PaXEHlXAsFuvxBQTXKb/2rVrQyYNye9wCxQ6eTt2cA7E5NGW6PQW7ZeQMfrgSzK6\nXy0qevrf5D+slcTvrrcj0Xq5/1DcxOcr9zJFp2PRvDP5x6i+V8B7i5k6ThunECQc39XC0TYmj+aN\nO8jL67ri81zSGKY0OUmLCsc2cTRNywsJH5KK0RbJ2H/cxZJp5xBVMJKmdVsAiByeDUBEdjoR2enE\nThvvfy1XXSM77tNWWoTJyK4/PQOA56TZNE9fwJjFn6Bvbye9eDfxZu3K0aycGP6ypIRnV5YzLC6c\nCWmDV+qyubKVYXHhnJafwIysnleP5gyLZc6wwP9gdTcx3cqZYxI5bVSCChSUkFW98DvK//MZEXlZ\nZF93AXpLGM0btmMvLmfPv17FnBCLMdpG+dufk3HJGSTPn411RI62Z4sQuBtbqPzgK/QR4Qi9nqS5\nMxF6HSUvv8f2Pz7uXy7PuvZ8AIqefAOkpO7bVRhjo8m54UIyLj4dQ8TAa3ncTS14HU7Ckrraorqb\nWyl++t8UPf3vvsFLAEKvRx8Rrl3Jczip/GAR+177iNGP3kFYUjzb738cU1wMiSdNx5afS+03K7QL\nHKUVtGze5U/F0JlNpJ8/n8yfn0XThm2YYqNpWLWBvY+9SsV7X2KMsWHNz6V+2RrtjXU6fw59zLTx\nhKUm4HO4qP16BV67g7DURBLmHM3w310HOh2OfZU0b9nJnr+/rKVEdI7foKfoqTeJP+4oDLYIKj9c\nDB0X5mKPnkDi3BnseOAJwjNSSD51DvaSctp2l5L9iwuImTqO0lc/oPw/n+Ntt5Oy4EQSjj8ac0Is\nscdM8F+d9zldCJOR2q9X0LpjL66aehrXbKa9uIzI3Cy8dgfFL7wDwKgHb8aan4spPgZPSxvmhFjC\nUrtS5IZ7PLRs2Y29qAx3cwue1nbqvl1Je1EZcTMmE5mXhXXUMFq376Xyw0XsuP8J/8/GTB1H0rxZ\nGGyRhGekEHv0+AOuIChHDqMtEuMB9nbqPHnunBfRE0cTPXF0wGPDUhOJnjCq64HzTjngGAzWCKwj\nuy4aBlo5HGiQ0Eno9SSecAyJJxwT8PmANZz9vEV/K7ixRxUQe9T+O1+GiqCTH4UQucB5QBra3gdv\nSil3DPbABkthYSE+JmhdQwIweVz4dPqgJ9KBRA7PpnX73h6TpKJj46KJaVZ+KG4ivK2VdksEUeHG\n/bbJ62ROjGPONu0XSPegZvI7j+FuaOLopCQ23XodcTGRCPS8XljJb2YO4ZPIDKZTyH9qBbd5fViy\n0pm9+VOMMTa23vkoJc+/7V/9CMSSpQUh1rHDyf/gab4uLMXx9CvEfPYFx9t1jF3ylf/YRSPnctz6\nD2nbsJ3bKtfwcNIEdn27jhHT81jm0DbEOXVU4N7VAyGlZG+Dg+NyYjgp78fXQ5j0Oq6a2nM5PZTS\nSpTQ43U4teX1josBBztfPG3tbL3zUcLTk8m88mx/4WDrziLadhYTkZvJhuvv9Z9cFz3+OnpLmH9p\nXB9hoW1HEcJoIP2i0xh5/0190lpMMbaAm0NmXXkOGReeTtVn37L70RcpekJr2Zx23ink3nIFFR8u\nouLdhWy/9zH2/ONloieNYdjNl1P+9udIn8ScHE/j6k3Y8nOxjcmjeuEyfG437vpm6pasQnq9xEwd\nB0DT+q1Ijxfp8ZI0bxaJc2fibm4hIiudiNxMjDFRuOsbadm6m6b123BW1jLk8jOJGjvc/72XvfUZ\n237/N5YefY72ATpOvHf9+VniZkyibtkajLYILFnpxM2YRNo587Bkp2OIsGCK11omd14QSZgzjdQF\nJ1G7ZCVlr39M/bI1JM6dQeqZc2lYsR5rfi72kgrK3vyY5sKteO0OEk6cjnVkDi2bdlL66geUvvK+\nfwwAlpwMRv/1DqInjUYfbsZgjaDkxXcpfuYtXHWN1Bw/nlNv/SWRwzLRmY0IvZ6MC09DF24OuOqa\nMGcaPrcHT2t7v1caO1e/E2YfRcLsowIe43O58bk9B1yR0RkMRI0d7v/OAbKvOS/guLKvOx97aQWO\n8moQQks9UcHBoFK/i5RQE9QZshBiPvAa8DFQDAwHVgkhLpJSfngIxjcotB2cAz9ndLnwmAa/K8VR\nnz7Ld8ddhLOyxv9YebMTg06QnxSBweUia9cW7JZIzho78DQdncmIztSzFZ7RFukv3Dr61xcCcOyS\nYr4vbqKmzc2eEWOYtvgTdsal8relJRQ1OHh0vlb423mCIvYTLG2zaCfl+2Yeyw8ry1m4swnTxBP4\nxecLGbvkK8LSkzn6i+dZPOYUvK3t7Lj/Seq/X4t72x5+jVb3/l1mOn+97BZ8BgNOj7a75rXT0v0r\nPlJKkDLgLx2vT/Lx1lr/7rptDjdDF35KvWHqYROV/69y1tRjL63ANnb4YVGY2ZkfLIwGf3MCR2UN\nOx96mor3vkQYDIx66GaS58/G3dTiX1YPltfhZN2lt/uv7O/66wtYMlMxWCP83c06jX9Bq01af83d\n6MLMjP7rb2ndWUz6uT/Dkp2O0Ot+VO2BPtxM6oITST51Nk1rtxCWkkB4hlaAnGDcuoEAACAASURB\nVH3NeWRfcx4NKzdQ+vL7VH7yNTVfaj3BO6++mxPjqFnYtY+A0OvRhZkJS0sice4MGlduoL2kgtSz\nTsYYbSN5/uweJ6PdGW2RWLLSSTp5Vt9xhpkZcvHpJM2dQekrHyB9PmKnFRCWmsTOh5+mad1W0s8/\nhRH33jigNCWAyLwsIvOyyLzs/2jesB1rfi46k5HkU7paZ+feegWOqlrqvl1F6pkn+f8/N67dTNUn\n32hX0oekYBmSim3siD4XnIb+6lKyrj6Ptt3FbGis7vPZD9R5RWc0BJ2S0Oc1Avy+GAzhGSn+uaIo\nypEv2JqFjcAvpZRfd3vsWOAxKWXgNaWf2KJFi+TtawUXjk/m4ol9/3F744K7MK5Yjf2tF7ion04d\nP9aqc27E09zGtM+eBeAPX+2lqMHOc2eO5P3fPUX4sy+T/dtryPvlRYO+hfcXO+p4ZEkJl01K4YXV\nFRidDtzmrl9Oj50+nLx4C44qrfvR2H/dTXhaUsDXuuuL3exZtZWIETm0uX3UtmmpW3c8fR+OkgrG\nPXkvKaefgJSSxaNO9hc8GWOjcdc3YrdEEN7extaxk1g/dSazP/o3RbmjWHbiadw1J5sXVlcw6+Wn\niWtpYMuCs9DVNbLgxgUMjdNWWz7cUsNHLy1kxrIviYuNhLXr0Xu9mJPiOW59yMao/xM6iyYDLfva\ny6r4bvbFeJpaMKckED1+lJYLPmsK5qQ47KWVZF93Po7yGvRhJq394U9Aer2Uv/0F0uul6tNvqflK\n64SWsuBE4qZPZNs9/8TrcJJ+7s9oKtza42Q+ad4sxj//IJ6WNio//prW7Xspe+szAExxUURPGsOQ\ny/4PU1w0QghKX/2QivcW0r5X2xNlzD9+h3VkjtZhY/tePE0txE6fiHR7cFTVYsvPJeOi0xB6Pe1F\n+zBE2Q76BPLHaN60g7K3PiNp7kwsOel4mtuIyM1k32sfIgwGrKOGYYyKxJwUD5JBr3VQFEVR+grJ\nmgUgnb7dkJZ1PB7S+ktDMrhdeIwmXllbOejBQlhyAjVbduNze9AZDVS0OEm1acvOQ4p30z5sCMNv\n7HcPuoOSn6Tl073ZUQh81bHDSLGZuOsLLa92X6ODvHgLYUnxfTo/fbmzjglp2h4Ebq+PdeUtuJPT\nqG3U0qh+MS2dsSmRxEz4I83rt5Jy+gmA1hmk4On72HrXo7Ru38vkfz/K519v5gVjOhO+X8yMLz9k\n5AbtSmpiZRkjNqym7kEXVbfcR/JqrcXeuAcfAOCZnKE8dOk0ar5eTvvNj3BaVRVC+qhvS8ExaQp5\nVSXYi8po27uPiOyQn35HDOn1sv2+J4ibOYm2ncXs/tuLeJpbtZx3n4+Mi8+g+Ln/oDMatLZ0LjfD\n776e+mVraNtdgs/jpfaef/pfb+cDTwJat66CZ+4j9ujxh2QFwtvuoP6HddQtWYXBGkHZfz4j+5rz\nMMXFsPHXD/gLNI2xUQy96VLspZVUffotFe8uJDwjhWmfP0dETgY+p4uar5fTtHYL9d+vperTb9n7\n5BuUvPAO9uLyHjnvkXlZVLy3kLI3Pu4aiBDYxgwn9cy5pJxxAglzpgFgGxP4int3lqyfbp7bRudh\nG53X9UCylkqYceFpP9GIFEVRlP+WYH8rFwI3A913Sv51x+MhqbCwEBiPvp+4S+9y4TgEaUgA5qR4\nXDX1rDzjOqZ+9BTlzU5GJ0XSsnU3NV9+R/oF8w/J+wKk2cxkxYRR1ODgqCE2TsvXfrl/fNk4Tn1x\nPfuanAF/rqbNxZ+/LSEv3sJjpw9nV50dt1fy2+OycHh8xFkMTMno2B06dlTPQiQgbsYkpn/7mrbR\niTWC00fmckybm6SrJ7P5lXxcn35F9rXns+PBp2D9NgCu/+zVPuM49vabeS3qb+R98jHR5WU458xi\n6F2/oLJVx5ycGFLsTXw7aQE7H3yKvDuuHpQTqcM1T3Tnw89Q+80K8v9yW599PQ6G9Pkoe/NT7GWV\n2PJzSTxpOkXPvEXRE6/7c9xN8TFIj5e9/3wFgL3/eq3Ha4z+252kn/szsjsKaL0OJ+uv/h22cSO1\n3tLfrqRu2Rq8be2sPvtGrKOGMe7pPxI5bHA29Gsq3ErZm59Q+vpHffqNb/ntIwCEpSWR99tr0EeE\nkzx/tj+VxVFRQ/3ydcQfe5T/ar7ObCJp7kyS5s5k8UefYNxTyvZ7/gk6HRNeepiYowpACNp2FRM9\nIR/7vkqa1m7B3dKKp6WN+JmT/R1ylP8dh+u/LcpPQ80XJdQEGyxcC3wkhLgRKAUygHbg0J31DpL+\nuiHhcOIxHppgwdhxgtG4ehMN7drOrKk2E+t+fjMA8ccFLkobDEII7j0hh3c2VXNht70GTHodyVYT\n72+u4Y3CSl48O58ka9fnL27QtrLfUdvOW+ureHeTtivt2ORI4iIGnvva2SnAZNCRFqWlJIy7ZD5c\nok2V9pIKtnQEC7qVWieSYb+9mj2PvsDQ269h5z3/wPLrO6mzt7Fl3GRm//kORqda6cp1CydyRA6V\nHy6i+oulHLvuA0yxUUF/T4e7pnVb2P3oCwAUXnEn05e8PijF+u7mVtZf/Xttp9AOBlsknuZWDNYI\nhF6Hu7mNmSv+g7uhGXdTC7WLl+OsqiXzyrMpeeFdrKOGkXb2yT1eVx9mZsJLf/Lf7yyi9LS0se+N\nj9lx/xN8N+tCCp65D3NyPG27S2jfU0r6haf1SZNrL6lg3WW3k3D8NPThYST97FgicjJACIROR+vO\nIlYuuB5vu5342dPIvPz/iDlqHI6yasLSkyi84i58Thdj/nEX4el9u5GFpSSQesaJ/X5Hppgopi7/\nDw0r1mOwRvSon+lsGRmenhzwtRVFURTlcBFUzQKAEMIAHAWkAuXACill4C0CQ0BnzcKVU1I5a2zf\nnPy3Z11GvUvy9s9vZOEV4wO8wo/naWtn4y/vo+qTb0j5+EVuKWzj3gnRNM27gOzrL2T4XdcN6vsN\n1H2L9rJkr9ZR5bpp6Zye39Wd6J2N1Ty1oqzH8dMyo7j3hJxBHYP0+ahbsgpLdgbNG7fjaW4l/fz5\n/oLRr4afhKepBYBFZ17EHx69qs/mbjVffc+m3zyEs7KWob++nNxbrxjUMR4O1l97NzVffc+oB29m\nw/V/AGDCSw+TeNKMoF6nfnkhm25+COn2EJaagLuxhbZdxYy8/9eknz+frXf+lfJ3F5I8fzaZl/8f\nlux0PK3thCX/+M5WgbQX7WPVOb/SUnq6McVFk37hqRijbVR/sYyUBSdS/flSahf/0Oc19JEWDNYI\n/46X4194SLVzVBRFUY44oVqzgJTSg1ancFjpb2XBZ3fgCbf2edzl8fHcqnIWjE7sceU9GIYIC9nX\nnU/VJ99QtXE3kc1hNM37BQBJJ8/8Ua85GM4rSPIHCxsqWnsECyWN2spCZkwYiREmThkZ7+9CNJiE\nTkf8sVOBnj2RO0/oRj14M5UfLSby9LlMOG5qwF2gE44/muMKP2TV2Tey+6/PE5mXRfKps/8nTgqd\n1XXULVtDxQeLyLrqHFIWnMjeJ9+gZdNONt70ADOWvO5vGXkg9n2VbLntz/7dvu0l5ejCzeT/5XbS\nz/0ZAPl/upX8P93a4+cMkQH6TB8kS1Y6U99/gprFP2CMsmKMttG2q5iiJ99gz99f9h/XsFzLfBx5\n/69JPnU20uNl2z3/wFFWReTwbNp2leCsqCH7uvOJmz5x0MepKIqiKP8rQr+X4UHqrFnor3VqtM5H\nRYA0pE+31/He5hoQcG2AnZ8HKjJP6+3dunQlQ8K7VjYGUtB4qAyNs/DEGcN5c30Va8ta8Pqkf2Oy\nvfV2xiZH8pdTBi/3/cdIXXAiqQv6TwHpLvOKs6j/YR3rr/k9Fe8tpOCZ+39Uu8BQzhN11TVSt3Q1\nkSNyaNmyi403/BHp9WKKjyHzyrMROh3TPnmGtj2lfH/S5aw+7ybiZk4mbuZkHBU1WLLS/GkyUkpa\nt++l4r2FuBtbKH3pPQCG3XIF+ohwkk6ehTkx7ifraBOWkkDGBaf678dNn8iQSxfQVLgVU0IsQqdj\nx/2Pk3zq8SSe2LVhTsFTf/Tfll4vtd+uIm7GpEM2zlCeL0poUXNFCYaaL0qoOeKDhU797dJrcrvI\nTNGunHc/af6hWLvyHh12cF+RwRpBxiVnUPrSe4wfmos+PIzZWz87JL2vgzE0zsLRmVF8u6eR3XV2\n8hIseH2SvfV25o2MP/ALhJDEE6dzYvE3FD39b7bf80+KnnqDnBsOTZep/wbp9VL83NtUf7EUfYQF\nQ6SFyo8WIzu2qweIKhjJ8LuvJ6pglP+kXmc2YR05lNF/uZ3NtzxM88YdXQXHQjD+hQdJOO4oCq/+\nHdWf92xqln7BfLKuOifwrpQhIqpgpP/22Mfu3u+xQq/vd6MqRVEURVEG7ogPFgoKCnhzbeA0JFdt\nA87KWnQTtJMtl9dHuE5Ld6lu1cow7G7fQY8h95YrKH3pPZJ278Q6eQz6sNDoQV6QoqVfrd7XTG58\nOPuaHDi9kty4A+8mHWqETkf2NedRt2Q1O+5/En14OJlXnBXUa4TKlZxdf3mO3Y++2OOxlP87kYyL\nTqdp3RYc5dVkX3s+YamJAX8+7eyTSZ4/G29bO2suuZXYqQXULV3FuktvJyw1EUd5Nbm3XUnaOT9D\nSom3zU5kXtah/2BHmFCZL0roU3NFCYaaL0qoCTpYEEKcAJwLJEop5wshJgE2KeXiQR/dIArUOnXx\naC0fWxeubVbm9Pj8ufF2txeANpf3oN/bFB+D22zG6HQSe8yEg369wRJjMTI2OZIX11SwcGcdZ3cU\ngA+NG9guqKGo4Ok/sPxnV7HjoafwOpwkzp0xaG04B4uU0r8Jn8/pwud2+/P/HeXV7H38dRLnzmDo\nry4lPDON1m27iTmqACHEgHes1oeb0YebmfbJM4C2z0Dx829T+80KUs+ay9CbLjs0H05RFEVRlCNK\nUJWgQogbgCeAnUBnha4duG+QxzVotJqFvpuyedrs/tv6jmDB5ZU4PD68Pondo60oDEaw4JOS7046\nHUfBWIb+8pKDfr3BdPFEra1jebOLvy0rJTMmjCHRYQf4qdBliIwg/fz5eFvb2XHf46w+9ya8jsB7\nSvT22TMv4aypH5RxuJtbqVn0A727jbVs3c23kxZQ8f5XtO0u4Yd5V7L06HOp+nwJ1V9+x7JZF+Bz\nexh+9w1EFYzEFGMjdtr4g97hW28JI+f6C5ny9j/J++01B/VaimbZssOuz4PyE1FzRQmGmi9KqAm2\nbcyvgOOllA8Bnfk524Cfrlp3gHqXLLRs2uG/rW/Xdm91uH2c+uJ6/r6s1J9+NBjBQpPdw9opMxGP\n/BG9JbROxMemWHn2/7pywW8/NrPf+o7DRcoZJ5Bw/NHk3n4Vjn2VbLrpAZo3bqdm8XKkN/D/z+ov\nlrL1zr+yZOpZ7PrrC/i61Qd0kl6v/3FPS1uPIMReVkXj2s3acVJS+PM7WHPBzdqGXd1s+e1fcJRV\nsf6a37P0mHNp2bwTZ3Ud6y69nbUX3YLBFsmUt/+pdqVWFEVRFCUkBJuGZEXbjA2g85KpEXAN2ogG\nWX81C00dG4IBiH3lMA7Wlmt9/T/fUed/rs198MFCTZtW/5AYeWg2fztYGdFdNRRDD8N6hd7MiXFM\nfPUvADir6yl5/m0q3vsSgMSTppN19Xl4nU4SOjbFay/ax/pf3Msog5XwzFR2/ekZXDX15N11HZ7W\nNuq+XUXKghPY+eBTFD31JuOe/AObb3kYvSWcvDuuwZKdQeEVd+AoryYiNxN7aQU+h/ZXouiZt0i/\n6DQih2XSsHojDcvXM+w3P6e9aB86s4mhN12GKS6GxjWbcNc3ETOtAHNC7E/zxSlBUXnFykCpuaIE\nQ80XJdQEGywsAW4H7u/22C+BrwdtRIeIvlew4G5oAiBu1mTcV14MRbBkTwMAQ6LD/PsNtA/CykJF\ni3YFOjHyp+2A1B8hBM+eORKL8cjbn2Dk/TeRceGp1H+/jqbCLZS//QXVXywDnY5Jbz5K2b8/oeKd\nhejMJmateofwtCQ2/eYhSl/9gLK3PsPbpq061X6zgvrv1yI9XgqvuBMAd0MzG35xr/+9dOFmTLHR\ntO0sxhgbzTFfv8zSY85l4433MfW9xyl98V0Mtkiyrj0PQ0TPoEztBaAoiqIoSigKNli4AfhICHEl\nYBVCbAdagFMGfWSDxL/PQq/zYJ/Ljc5sYvK//8668hYo2kVxR4DQ6tJSTXQCWgchWChucKATkBEV\nWilI3R3OdQr7I4TAOmoY1lHDAIieMg58Prbe9Sirz77Rf1zs0RNYs3cn09OSyLn+Qmq/XoFtTB7V\nny8lespYKt5dCEDSvFnULPqBoTdfTnhqIqaEWEpf+YDYoycw5LIFCCFoXLMJY2w0YUnxjH7kt6y/\n+nesu+JOahf/QNp5p/QJFJTDk+qFrgyUmitKMNR8UUJNUMGClLJCCDEZmAxkoqUkrZRSHnx/0UOs\ndxqSz+1BGLWPb9ZrkUSLUwsM6tu1YCEhwnTAYOHtjdWUNTm4dlo6Jn3gK/NFDQ5SrGZMhiPvyv3h\nZsjFpwPgbbOz/Y//YtRDv6G9uJz0C+ZTWKll2Fmy0jl2jbZRmauhGWO0lb2PvcLuv71M7h3XMPbx\ne3q0v42fNaXHe0RPHO2/nXLaHJrWbaHoyTdApyPj/PmH+iMqiqIoiqIMmqCCBSHEH3o9NAaYJ4Rw\nAvuAz6WUVYM1uMHQWbPQOw1Juj3oOoMFQ+CC3vgII9WtLnxSBtynoaHdzdMrygA4bmgsY1MiA75O\ncYOdzJgj88r94Sr7FxeQftFpGG1d/8+mB2ixaoqxAZBzw8VkXXO+f84EY8Q9N5A0bxbGaJvaz+AI\noq78KQOl5ooSDDVflFAT7KXuPOA24DhgWMeftwHjgWuBPUKIuYM6wkHSu8GPz+1GZ9RqCIz9rAjE\nRxiR9L8xW1272397Z217wGNcXh9lzU4VLISg7oHCQPyYQKFTzJSxKlBQFEVRFOWwE2ywoAPOlVLO\nkFKeL6WcAZwNeKWURwHXAQ8N9iAPRuc+C73bgcoAaUi9ZXbk8Ve2BO7TX28/cLCwr9GJT0KWChZC\nnuptrQRDzRdloNRcUYKh5osSaoINFk4CPuz12MfAyR23XwVyDnZQh0LflYWuNCRTt+2dk7q1N81L\n0ApRSxsDBwt1HbUNObFh7K6zBzymuFF7PDP68N0VWVEURVEURfnfFGywsBst3ai7azoeB4gHAl9i\n/4kUFBQAAQqcXW5ERxpS98LjVFtX4eqwOAsC2NfkCPjanWlIBalWypqdeH2yzzFFHZ2Q0rvtZaCE\nJpUnqgRDzRdloNRcUYKh5osSaoINFq4AfiOEKBVCLBdClAK3AD/veH448LvBHOBg6R0sSE/3Aueu\nryGtW7AQFWYgMdJEaVM/aUjtbqxmPTmx4Xh8sk+6UrPDw5I9jWREh/XbKUlRFEVRFEVRQlVQZ7BS\nyrVALnA+8ChwAZDb8ThSyiVSymcGfZQHwV+z0DsNydVVs2DQCS6blEJObBijkiL8x+h1glSbiaqW\nwBtU17e7ibUYSe/YP6F3UPH17gbKmp1cMzVtsD6OcgipPFElGGq+KAOl5ooSDDVflFATdHsXKaUb\nWHoIxnJI6XoXOHdbWQA4ryCZ8wqS2VLVBsBZYxIBsBj1NNoDryw02N3EhhtIj9JWI0oaHRw1JMr/\nfFmzkzCDjglp1kH9LIqiKIqiKIry3xB0sCCESAKmoNUn+M/ApZTPD+K4Bk1/+yx0r1nobmSihUdO\nySW/Y4Uh3KjD7gncOrXJ4SE5IQJbmIEIk56aVneP5yuanaTaTIgAezQooUfliSrBUPNFGSg1V5Rg\nqPmihJpgN2U7Ha3j0U4gH9gMjAaWASEZLHTq3Q1JejzoI/p2KBJCMCa5q/9+mEHf7z4LjXYPNrP2\nFUaFGWhy9AwWypudDIlWLVMVRVEURVGUw1OwVbf3AZdJKccDbR1/XgWsGfSRDZLOmoW+3ZA86AwH\njpXCjDocAVYW3F4f7W4fUeGdwYKeJoe36/WlpLLVRYpNdUE6XKg8USUYar4oA6XmihIMNV+UUBNs\nsDBESvmfXo+9BFw8SOM5ZHS9PqnP7UaY+qYh9RZu1OH0+PxtUd/bVM0Vb2+lulUreo4O61pZKCxv\nYeGOOgA+3lqL2yvJiVX7KyiKoiiKoiiHp2CDheqOmgWAIiHENGAooB/cYQ2e/vZZkJ4Brix0tFV1\ndqwuPLG8jJJGB/cvLgK0IKHzTwk8urQEl8fHB5tryE+K4LihMYP0SZRDTeWJKsFQ80UZKDVXlGCo\n+aKEmmCDhWeAzln8KPA1sB54fDAHdSgELHA2HThYCDdqcZDD4+PjrbUARJj07OrYsbl7sADglbCr\nzk55s5OxyZHoexdLKIqiKIqiKMphIthg4c9SyncApJQvA3nARCllSG7EBt1qFnp9UunxogvQDam3\nzpUFu9vLU8v3kWI18eq5+f7no8K0YMIW1hV4LNnbgFdChipuPqyoPFElGGq+KAOl5ooSDDVflFAz\n4G5IQgg90CqEiJZSOgGklCWHbGSDLHDr1IGsLGjBQnWbG6dXclp+AhEmvdZS1e3zryh0t3hXA4Dq\nhKQoiqIoiqIc1ga8siCl9AI7gLhDN5zB11Wz0PXYvjc/wVVTH9TKQlnH7syx4drP/Ov04Vw8Idkf\nLBi7vUGjwwPg36xNOTyoPFElGGq+KAOl5ooSDDVflFAT7KZsrwEfCyH+DuwDZOcTUsrFP2YAQggd\nsBrYJ6U8tZ9jJgPfA+dIKd/teKwIaAJ8gFtKOWV/79O9wHnTr+7XXtd44LrszpqF8mYtWIiL0IKF\n9KgwLpyQ4j/ulJHx6ISgvMXJe5tqyIwOw2IK2bpvRVEURVEURTmgYGsWrgVigHuAZ4HnOv579iDG\ncCOwpb8nO4KJh4Avej3lA46VUo7fX6DQtc9C3+cGsrLQmYbUe2WhN6Nex2n5Cf5WqbGWoDfHVn5i\nKk9UCYaaL8pAqbmiBEPNFyXUBHVGK6XMHsw3F0KkA/OA+4Ff93PYDcDbwOTeP04QwU6grkTBpCH9\nUNIEHDgIGJei7f581tik/R6nKIqiKIqiKKEu2JUFhBAnCCGeE0J81HF/ohBi9o98/0eBW+iWztTr\nvVKB06WUT6AFB91J4EshxCohxJX9vUF/+ywAAypwDjP2/IrCD5C6lGw1s/CK8UxKtx3wtZXQovJE\nlWCo+aIMlJorSjDUfFFCTVDBghDiBuAJYCcws+NhB3BfsG8shPgZUCWlLEQLBAJtSPA34LbuP9bt\n9jFSygloKxO/EELs929X58KClF1xiW4AwUJEt+Cgc9VAURRFURRFUf4XBJtY/ytgjpSySAjReRK/\nDRj+I977GOBUIcQ8IBywCiFellJe3O2YScCbQggBxAMnCyHcUsoPpZQVAFLKGiHEe8AUoE+i39//\n/nf2lDv5a/U49DqBNdKK09fGKF0Ewmjw5wZ2RvK9769c/j2XJjlYcPJswgy6Ax6v7h++97vniYbC\neNT90L6v5ou6P9D7nY+FynjU/dC+3/lYqIxH3Q+d+xs3bqSpSUuLLykpYdKkScyZM4dDTXS/0n7A\ng4WoBlKklF4hRL2UMlYIEQbslVKmHOjn9/O6s4Cb++uG1HHMC8BHUsp3hRAWQCelbBVCRAALgXul\nlAt7/9wjjzwi3/SN59PLCzDoBJ42O18N1b7YEff9iqwrzv6xw1aOMMuWLfP/pVSUA1HzRRkoNVeU\nYKj5ogzU2rVrmTNnTqDMnEEVbM3CEuD2Xo/9Evh6cIYDQoirhRBXBXiqe1STBCwTQqwDlqMFEX0C\nBei7z4J0u7te0OUZlDErRwb1j7MSDDVflIFSc0UJhpovSqgxBHn8DcBHHQXFViHEdqAFOOVgBiGl\n/Bb4tuP2U/0cc3m323uBgoG+vqCrwNnn7goQvHbHjxqvoiiKoiiKovwvCGploaNOYDJwDnA+cAkw\nRUpZeQjGNigKCwv9qwqlr35Ay9bd/udUsKB01z1fVFEORM0XZaDUXFGCoeaLEmqCWlkQQvwNeE1K\nuQJYcWiGNPh0OoGUks2/ebjH4z6H8ycakaIoiqIoiqKEvmBrFgTwgRBipxDiXiHEj+mC9F9VUFCA\nTgik19vnOWNs9E8wIiVUqTxRJRhqvigDpeaKEgw1X5RQE2wa0o1AOnAdkAEsF0KsEUL0t/tySNAL\nkO6ewULyqXPIueGin2hEiqIoiqIoihL6gt7BWUrpk1J+2VFwPBqoA/486CMbJIWFheh1Aunt2fko\n9ay5A9qUTfnfofJElWCo+aIMlJorSjDUfFFCTdDBghAiQghxoRDiE2AH4EErdA5ZOiHw9VpZ0JmM\nP9FoFEVRFEVRFOXwEFSwIIT4D1AFXAV8DGRKKedJKV89FIMbDFrNAkhPz5UFnVEFC0pPKk9UCYaa\nL8pAqbmiBEPNFyXUBJuHswptp+WSQzGYQ0UnBNLdK1gwq2BBURRFURRFUfYn2ALnPwFOIcR8IcRl\nQojLO/87ROM7aJ37LPh6BQtCrSwovag8USUYar4oA6XmihIMNV+UUBPsPgunA68CO4F8YDNakfMy\n4PlBH90g0QkRIA1JFTcriqIoiqIoyv4EW+B8H3CZlHI80Nbx51XAmkEf2SApKChArxN9VhZUgbPS\nm8oTVYKh5osyUGquKMFQ80UJNcEGC0OklP/p9dhLwMWDNJ5DIlCBs0pDUhRFURRFUZT9CzZYqBZC\nJHXcLhJCTAOGAvrBHdbgKSwsRB+owNmk0pCUnlSeqBIMNV+UgVJzRQmGmi9KqAk2WHgG6FwfexT4\nGlgPPD6YgxpsOgE+T699FtTKgqIoiqIoiqLsV1CX16WUD3e7/bIQ4hsgQkq5dbAHNlgKCgpYVRyg\nwFnVLCi9qDxRJRhqvigDpeaKEgw1X5RQc1C5OIfLfgt6IZC9VhaE6oakJawAhgAAEq1JREFUKIqi\nKIqiKPsVbBrSYae/fRZU61SlN5UnqgRDzRdloNRcUYKh5osSao74YAEC77Mg9CFbk60oiqIoiqIo\nIeGIDxYKCgrQ6fquLChKbypPVAmGmi/KQKm5ogRDzRcl1BzxwQIQsHWqoiiKoiiKoij7d8QHC501\nC73TkBSlN5UnqgRDzRdloNRcUYKh5osSav4nqnyFEP40pCnvP47QHfExkqIoiqIoiqIctCM+WCgo\nKGBXddfKgiUzjbCUhJ94VEooUnmiSjDUfFEGSs0VJRhqviih5n/iErtOCHxubZ8FYVBdkBRFURRF\nURRlII74YKGwsBABSK+2siAMR/xiivIjqTxRJRhqvigDpeaKEgw1X5RQc8QHC9Cxz0LHyoLOqFYW\nFEVRFEVRFGUgjvhgoaCgAAT4PGplQdk/lSeqBEPNF2Wg1FxRgqHmixJqjvhgAbQP2bnPgs6oggVF\nURRFURRFGYgjPlgoLCxECKF1QxICoVdpSEpgKk9UCYaaL8pAqbmiBEPNFyXUHPHBAoBOgM/tQahV\nBUVRFEVRFEUZsCM+WCgoKEAILQ1Jp+oVlP1QeaJKMNR8UQZKzRUlGGq+KKHmiA8WAARagbNaWVAU\nRVEURVGUgTvigwV/zYLbi05tyKbsh8oTVYKh5osyUGquKMFQ80UJNUd8sABazYJUKwuKoiiKoiiK\nEpQjPljQahYEPo9XdUJS9kvliSrBUPNFGSg1V5RgqPmihJojPliAjn0WPB61x4KiKIqiKIqiBOGI\nDxa0mgWtG5JKQ1L2R+WJKsFQ80UZKDVXlGCo+aKEmiM+WAAQCK0bkmqdqiiKoiiKoigD9pMHC0II\nnRBirRDiw/0cM1kI4RZCLOj22FwhxDYhxA4hxG39/ax/nwWvT3VDUvZL5YkqwVDzRRkoNVeUYKj5\nooSanzxYAG4EtvT3pBBCBzwEfNHrsceAk4B84DwhxIj+XkPrhqQKnBVFURRFURQlGD9psCCESAfm\nAc/u57AbgLeB6m6PTQF2SimLpZRu4E3gtEA/7N9nwesBfSjERkqoUnmiSjDUfFEGSs0VJRhqviih\n5qc+e34UuAWQgZ4UQqQCp0spn0DbiLlTGlDa7f6+jscCEnSmIamaBUX5//buPWayur7j+PuzrChd\nBZcoqFxWFPGCuA/LRSkalS1ivaCpaYOtgpa0RvHS1laBNlUTtWqi9VoSvFCvJfVWsCF162KbmhTF\nLgMrIrBcXC5lLQWe6mrUffbbP855ducZ53l2xn0us/O8X8lkzzlzzpzf2fnkyfnO+f3OkSRJGtSS\nnT0neSGwrao6SZ7DzGJg2geBWccjDGLLli1cdcW/c9OWWyFh00UXcdxxx+3qEzhdwTvv/DOf+cyR\nao/zoz1vXpx33nnnnV/M+c2bNzM5OQnA1q1bOfHEE1m/fj0LLVV9f9Rf+B0n7wZeAewADgAeBnyl\nqs7uWufW6UngEcB24I9puiS9vaqe3653PlBV9d7e/WzcuLGu+vkhrPubd7Lfg/fnpC9+eCEPS5Ik\nSVpwmzZtYv369f1+bJ9XS9YNqaourKojq+pxwFnAld2FQrvO49rXUTTjFl5XVZcDVwNHJ1mTZP92\n+753U9r1nIUdU8S7IWkO01W8NAjzokGZFQ3DvGjUrFzqBvRK8hqaqwQX97y16xJIVU0leT2wgabg\n+WRV3TDbZzZPcJ4iK5Z6iIYkSZK071iybkiLZePGjfXdXx7KsX95IQccdijrPv2+pW6SJEmStFfG\nvhvSYgo+Z0GSJEka1tgXC51Op3ko29ROiwXNyX6iGoZ50aDMioZhXjRqxr5YANqHsk35UDZJkiRp\nCGN/9jwxMbHrbkgrvBuS5jB9L2NpEOZFgzIrGoZ50agZ+2IBYEV7ZcFuSJIkSdLgxr5Y6HQ6zQBn\niwXtgf1ENQzzokGZFQ3DvGjUjH2xADTdkKZ2+lA2SZIkaQhjXyw0Yxbabkg+lE1zsJ+ohmFeNCiz\nomGYF42aZXH2vOsJzl5ZkCRJkgY29sVCp9PZ3Q3JMQuag/1ENQzzokGZFQ3DvGjUjH2xANNjFnZY\nLEiSJElDGPtiYWJighXEAc7aI/uJahjmRYMyKxqGedGoGftiAdj1ULb4BGdJkiRpYGN/9tzpdEgV\nVNkNSXOyn6iGYV40KLOiYZgXjZqxLxYAVtROALshSZIkSUMY+2JhYmKCTE0B2A1Jc7KfqIZhXjQo\ns6JhmBeNmmVx9pyq5t/9Vi5xSyRJkqR9x9gXC51OhxU7vbKgPbOfqIZhXjQos6JhmBeNmmVx9pyp\ndsyCA5wlSZKkgY19sTAxMUF2OsBZe2Y/UQ3DvGhQZkXDMC8aNWNfLACsmC4W7IYkSZIkDWzsz547\nnc7uKwsOcNYc7CeqYZgXDcqsaBjmRaNm7IsFoKtYWBaHK0mSJM2LsT97dsyCBmU/UQ3DvGhQZkXD\nMC8aNWNfLABk161TLRYkSZKkQY19sdDpdLpunTr2h6u9YD9RDcO8aFBmRcMwLxo1y+LsOWU3JEmS\nJGlYY18sTExMkCm7IWnP7CeqYZgXDcqsaBjmRaNm7IsF6L4bksWCJEmSNKixLxZmPmdh7A9Xe8F+\nohqGedGgzIqGYV40apbF2fOuAc6OWZAkSZIGNvbFwsTEBHjrVA3AfqIahnnRoMyKhmFeNGrGvlgA\nxyxIkiRJv46xLxZmjFmwG5LmYD9RDcO8aFBmRcMwLxo1Y18sQNcTnFcsi8OVJEmS5sXYnz3PeM6C\nVxY0B/uJahjmRYMyKxqGedGoGftiAQDHLEiSJElDW/JiIcmKJJuSXN7nvTOTXJvkmiTfSXJq13u3\nd7832+d3Op3dt061WNAc7CeqYZgXDcqsaBjmRaNmyYsF4E3A92d57xtVtbaqjgfOBT7R9d5O4DlV\ndXxVnTzbh2/ZsmX3lQW7IWkOmzdvXuomaB9iXjQos6JhmBcNqtPpLMp+lrRYSHI48AJmFgG7VNVP\nu2YfSlMg7NqcAdq/ffv2risLo1AbaVRNTk4udRO0DzEvGpRZ0TDMiwZ17bXXLsp+lvrs+W+BvwBq\nthWSvDTJDcDXgD/sequAf01ydZI/mnMv01cWvBuSJEmSNLAlO3tO8kJgW1V1aK4SpN96VfVPVfVk\n4KXAO7veOrWq1tFcmTgvSd/bB9xzzz2kHLOgPdu6detSN0H7EPOiQZkVDcO8aNSkatYf9Rd2x8m7\ngVcAO4ADgIcBX6mqs+fY5hbgpKq6r2f524AfV9UHerd57WtfW9u3b981v3btWiYmJubnIDRWOp2O\n2dDAzIsGZVY0DPOi2XQ6nRldj1atWsVFF13U98f2+bRkxcKMRiTPBt5cVWf2LH98Vd3STq8DLquq\nI5L8BrCiqn6SZBWwAXhHVW1Y9MZLkiRJY2rlUjegV5LXAFVVFwMvS3I28AvgZ8DvtasdCnw1SdEc\nw+ctFCRJkqT5NRJXFiRJkiSNnrG9PVCS5yf5QZKbkrx1qdujxZHk8CRXJrk+yeYkb2yXr06yIcmN\nSb6e5KCubS5IcnOSG5I8r2v5uiTXtRn6YNfy/ZNc2m7zn0mOXNyj1HzqfTCkWdFskhyU5Ivt9399\nkqebF/WT5E+TfK/9nj/ffrdmRQAk+WSSbUmu61q2KPlIck67/o1t7509GstiIckK4KPAGcCxwMuT\nPGlpW6VFsgP4s6o6FjiF5k5ZTwLOp3nI3xOBK4ELAJI8haZ725OB3wb+Lsn0YKGLgHOr6hjgmCRn\ntMvPBe6rqicAHwTetziHpgXS+2BIs6LZfAi4or1D31rgB5gX9UjyGOANwLqqehpNd+mXY1a02yU0\n56jdFjwfSVYDfw2cBDwdeFt3UTKbsSwWgJOBm6vqh1X1S+BS4CVL3CYtgqq6p70dL1X1E+AG4HCa\n7//T7WqfprkVL8CZwKVVtaOqbgduBk5O8ijgYVV1dbveZ7q26f6sLwHrF+6ItJDS/8GQZkW/IsmB\nwLOq6hKANgeTmBf1tx+wKslKmjs+3oVZUauqvgXc37N4IfNxWjt9BrChqiar6gGaGwQ9f0/tHddi\n4TDgjq75O9tlWkaSPBaYAK4CDq2qbdAUFMAh7Wq9WbmrXXYYTW6mdWdo1zZVNQU8kOTgBTkILbR+\nD4Y0K+rnKODeJJek6bZ2cZo785kXzVBVdwPvB7bSfO+TVfUNzIrmdsgC5mOyzcdsnzWncS0WtMwl\neShNNf2m9gpD70j++RzZv+D3ONb8y68+GHI2ZkXQdCVZB3ysfSDodppuA/5t0QxJHk7zy+4a4DE0\nVxj+ALOi4YxMPsa1WLgL6B7sc3i7TMtAe9n3S8Bnq+qydvG2JIe27z8K+FG7/C7giK7Np7My2/IZ\n2yTZDziw90GB2iecCpyZ5FbgH4DTknwWuMesqI87gTuq6rvt/Jdpigf/tqjXbwG3VtV97a+6XwV+\nE7OiuS1GPn6t8+NxLRauBo5OsibJ/sBZwOVL3CYtnk8B36+qD3Utuxx4VTt9DnBZ1/Kz2jsHHAUc\nDXynvQQ4meTkdiDR2T3bnNNO/y7NQCTtY6rqwqo6sqoeR/M34sqqeiXwNcyKerTdA+5Icky7aD1w\nPf5t0a/aCjwjyUPa73g9zU0UzIq6hZm/+C9GPr4OnJ7mzm6rgdPbZXOrqrF80QzYuJFmIMj5S90e\nX4v2vZ8KTAEd4BpgU5uFg4FvtJnYADy8a5sLgC00g6Gf17X8BGBzm6EPdS1/MPCP7fKrgMcu9XH7\n2uvcPBu4vJ02K75my8lamh+jOsBXgIPMi69ZsvK29nu/jmag6YPMiq+u7+8LwN3Az2mKy1cDqxcj\nHzQFyc3ATcDZg7TXh7JJkiRJ6mtcuyFJkiRJ2ksWC5IkSZL6sliQJEmS1JfFgiRJkqS+LBYkSZIk\n9WWxIEmSJKkviwVJkiRJfVksSJIkSerLYkGSloEkxyS5JslkktcvdXv6SXJbktOWuh2SpN0sFiRp\nhCX5dpKjkxyV5L/24qPeAlxZVQdV1Ufnq32SpPFmsSBJIyrJSuDIqtoCnADsTbGwBrh+XhomSVo2\nLBYkaXQdB3y/nT4RuGa2FZM8Kck3k9yfZHOSF3e9txF4LvCxJP+X5Og+2781yZ3t+zckeW7X8i3t\n8u8leWnXNrcl+fMk1yb5cZKPJzkkyRXt+huSHNSz/vlJrk/yv0k+mWT/WY7n0Um+lORHSW5J8oY9\ntVWSNP8sFiRpxCR5VZL7gW8BpyS5D3gz8J4k9yVZ07P+SuBrwL8AjwTeCHw+yRMAqmo98B/AeVV1\nYHulonv7Y4DzgBOq6kDgDOD29u0twKnt8ncAn0tyaNfmvwOsB44BzgSuAM4HHgHs17al2+8DpwOP\nB54I/FWf4097PNcAj24//01JTt9DWyVJ88xiQZJGTFX9fVWtpul29AxgLbC5HW9wcFX9sGeTZwCr\nquq9VbWjqr4J/DPw8gF3OQXsDzw1ycqq2lpVt7Vt+XJVbWunvwjcDJzcte1HqureqvpvmoLk21V1\nXVX9AvgqcHzPvj5SVXdX1QPAu2iKh14nAY+oqndV1VRV3Q58AjhrrrbOJcm6JK9L8s4kL0nysiSf\nGvD/R5KWLYsFSRohSVa3XYkeAE4B/g24EXhie1Wh95d6gMcAd/Qs+yFw2CD7rKpbgD8B3g5sS/KF\nJI9q23N2exel+9urHcfSXDWYtq1r+md95h/as7s7e9r46D5NWgMc1h7vfe1+LwAOmaWt/T6j1yOB\nHwBPqarLqurLwLMH2E6SljWLBUkaIVV1f3tV4TXAJ6rqYJruRS9qryp8uM9mdwNH9Cw7ErhriP1e\nWlXPojlRB3hvkiOBi4HXVdXqtl3XAxnuqGbobucamrb3ugO4tT3eg9t9H1RVL56lre/Z006r6us0\n3Z8+B5DkFODavTgOSVoWLBYkaTSdAGxqp4/vmu7n28BPk7wlycokzwFeBFw6yI7aZzA8tx1s/Aua\nKwI7gVXtv/cmWZHk1cBTf62j2e28JIclORi4cJY2fgf4cXs8D0myX5Jjk5w4R1unj+WSOboXnQZs\nbKfPAT6T5EV7eTySNNYsFiRpNK0DNrUn1TuqanK2Favql8CLgRcA9wIfBV5ZVTd1rzbHvh5M8+v8\n/9D80v9I4IKqugF4P3AVcA9NF6RvzfGZc+1j2heADTQDp2+mGbcwY/uq2klT7EwAtwE/Aj4OHDhb\nW7s+44ieNgKQ5ADg/q7/x58AD2dmtylJUo9UDfK3XZKkvZPkNuDcqrpygT7/QUAHeFpVTS3EPiRp\nuVm51A2QJGk+tFdYjl3qdkjSOLEbkiRpsXgpW5L2MXZDkiRJktSXVxYkSZIk9WWxIEmSJKkviwVJ\nkiRJfVksSJIkSerLYkGSJElSXxYLkiRJkvqyWJAkSZLUl8WCJEmSpL7+H0qRAzTNRZe8AAAAAElF\nTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa45047b0b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import numpy as np\n",
    "from IPython.core.pylabtools import figsize\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "figsize( 12.5, 5 )\n",
    "\n",
    "sample_size = 100000\n",
    "expected_value = lambda_ = 4.5\n",
    "poi = np.random.poisson\n",
    "N_samples = range(1,sample_size,100)\n",
    "\n",
    "for k in range(3):\n",
    "\n",
    "    samples = poi( lambda_, sample_size ) \n",
    "    \n",
    "    partial_average = [ samples[:i].mean() for i in N_samples ]\n",
    "    \n",
    "    plt.plot( N_samples, partial_average, lw=1.5,label=\"average \\\n",
    "of  $n$ samples; seq. %d\"%k)\n",
    "    \n",
    "\n",
    "plt.plot( N_samples, expected_value*np.ones_like( partial_average), \n",
    "    ls = \"--\", label = \"true expected value\", c = \"k\" )\n",
    "\n",
    "plt.ylim( 4.35, 4.65) \n",
    "plt.title( \"Convergence of the average of \\n random variables to its \\\n",
    "expected value\" )\n",
    "plt.ylabel( \"average of $n$ samples\" )\n",
    "plt.xlabel( \"# of samples, $n$\")\n",
    "plt.legend();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Looking at the above plot, it is clear that when the sample size is small, there is greater variation in the average (compare how *jagged and jumpy* the average is initially, then *smooths* out). All three paths *approach* the value 4.5, but just flirt with it as $N$ gets large. Mathematicians and statistician have another name for *flirting*: convergence. \n",
    "\n",
    "Another very relevant question we can ask is *how quickly am I converging to the expected value?* Let's plot something new. For a specific $N$, let's do the above trials thousands of times and compute how far away we are from the true expected value, on average. But wait &mdash; *compute on average*? This is simply the law of large numbers again! For example, we are interested in, for a specific $N$, the quantity:\n",
    "\n",
    "$$D(N) = \\sqrt{ \\;E\\left[\\;\\; \\left( \\frac{1}{N}\\sum_{i=1}^NZ_i  - 4.5 \\;\\right)^2 \\;\\;\\right] \\;\\;}$$\n",
    "\n",
    "The above formulae is interpretable as a distance away from the true value (on average), for some $N$. (We take the square root so the dimensions of the above quantity and our random variables are the same). As the above is an expected value, it can be approximated using the law of large numbers: instead of averaging $Z_i$, we calculate the following multiple times and average them:\n",
    "\n",
    "$$ Y_k = \\left( \\;\\frac{1}{N}\\sum_{i=1}^NZ_i  - 4.5 \\; \\right)^2 $$\n",
    "\n",
    "By computing the above many, $N_y$, times (remember, it is random), and averaging them:\n",
    "\n",
    "$$ \\frac{1}{N_Y} \\sum_{k=1}^{N_Y} Y_k \\rightarrow E[ Y_k ] = E\\;\\left[\\;\\; \\left( \\frac{1}{N}\\sum_{i=1}^NZ_i  - 4.5 \\;\\right)^2 \\right]$$\n",
    "\n",
    "Finally, taking the square root:\n",
    "\n",
    "$$ \\sqrt{\\frac{1}{N_Y} \\sum_{k=1}^{N_Y} Y_k} \\approx D(N) $$ "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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evZk9ezZ//vknaWlprrbuuVm0aJGr0lK5cmXKlSuHt7d3rjGePHmSChUqUK1a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\nLK+//jpfffUVR44coU6dOtx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POhO42RhzNK98LncfArCaDaVNncSXO61Zi6N3H9MKQSmg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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa44d4402e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "figsize( 12.5, 4)\n",
    "\n",
    "N_Y = 250 #use this many to approximate D(N)\n",
    "N_array = np.arange( 1000, 50000, 2500 ) #use this many samples in the approx. to the variance.\n",
    "D_N_results = np.zeros( len( N_array ) )\n",
    "\n",
    "lambda_ = 4.5 \n",
    "expected_value = lambda_ #for X ~ Poi(lambda) , E[ X ] = lambda\n",
    "\n",
    "def D_N( n ):\n",
    "    \"\"\"\n",
    "    This function approx. D_n, the average variance of using n samples.\n",
    "    \"\"\"\n",
    "    Z = poi( lambda_, (n, N_Y) )\n",
    "    average_Z = Z.mean(axis=0)\n",
    "    return np.sqrt( (  (average_Z - expected_value)**2  ).mean() )\n",
    "    \n",
    "    \n",
    "for i,n in enumerate(N_array):\n",
    "    D_N_results[i] =  D_N(n)\n",
    "\n",
    "\n",
    "plt.xlabel( \"$N$\" )\n",
    "plt.ylabel( \"expected squared-distance from true value\" )\n",
    "plt.plot(N_array, D_N_results, lw = 3, \n",
    "            label=\"expected distance between\\n\\\n",
    "expected value and \\naverage of $N$ random variables.\")\n",
    "plt.plot( N_array, np.sqrt(expected_value)/np.sqrt(N_array), lw = 2, ls = \"--\", \n",
    "        label = r\"$\\frac{\\sqrt{\\lambda}}{\\sqrt{N}}$\" )\n",
    "plt.legend()\n",
    "plt.title( \"How 'fast' is the sample average converging? \" );"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As expected, the expected distance between our sample average and the actual expected value shrinks as $N$ grows large. But also notice that the *rate* of convergence decreases, that is, we need only 10 000 additional samples to move from 0.020 to 0.015, a difference of 0.005, but *20 000* more samples to again decrease from 0.015  to 0.010, again only a 0.005 decrease.\n",
    "\n",
    "\n",
    "It turns out we can measure this rate of convergence. Above I have plotted a second line, the function $\\sqrt{\\lambda}/\\sqrt{N}$. This was not chosen arbitrarily. In most cases, given a sequence of random variable distributed like $Z$, the rate of convergence to $E[Z]$ of the Law of Large Numbers is \n",
    "\n",
    "$$ \\frac{ \\sqrt{ \\; Var(Z) \\; } }{\\sqrt{N} }$$\n",
    "\n",
    "This is useful to know: for a given large $N$, we know (on average) how far away we are from the estimate. On the other hand, in a Bayesian setting, this can seem like a useless result: Bayesian analysis is OK with uncertainty so what's the *statistical* point of adding extra precise digits? Though drawing samples can be so computationally cheap that having a *larger* $N$ is fine too. \n",
    "\n",
    "### How do we compute $Var(Z)$ though?\n",
    "\n",
    "The variance is simply another expected value that can be approximated! Consider the following, once we have the expected value (by using the Law of Large Numbers to estimate it, denote it $\\mu$), we can estimate the variance:\n",
    "\n",
    "$$ \\frac{1}{N}\\sum_{i=1}^N \\;(Z_i - \\mu)^2 \\rightarrow E[ \\;( Z - \\mu)^2 \\;] = Var( Z )$$\n",
    "\n",
    "### Expected values and probabilities \n",
    "There is an even less explicit relationship between expected value and estimating probabilities. Define the *indicator function*\n",
    "\n",
    "$$\\mathbb{1}_A(x) = \n",
    "\\begin{cases} 1 &  x \\in A \\\\\\\\\n",
    "              0 &  else\n",
    "\\end{cases}\n",
    "$$\n",
    "Then, by the law of large numbers, if we have many samples $X_i$, we can estimate the probability of an event $A$, denoted $P(A)$, by:\n",
    "\n",
    "$$ \\frac{1}{N} \\sum_{i=1}^N \\mathbb{1}_A(X_i) \\rightarrow E[\\mathbb{1}_A(X)] =  P(A) $$\n",
    "\n",
    "Again, this is fairly obvious after a moments thought: the indicator function is only 1 if the event occurs, so we are summing only the times the event occurs and dividing by the total number of trials  (consider how we usually approximate probabilities using frequencies). For example, suppose we wish to estimate the probability that a $Z \\sim Exp(.5)$ is greater than 5, and we have many samples from a $Exp(.5)$ distribution. \n",
    "\n",
    "\n",
    "$$ P( Z > 5 ) =  \\frac{1}{N}\\sum_{i=1}^N \\mathbb{1}_{z > 5 }(Z_i) $$\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.0001\n"
     ]
    }
   ],
   "source": [
    "N = 10000\n",
    "print( np.mean( [ np.random.exponential( 0.5 ) > 5 for i in range(N) ] ) )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### What does this all have to do with Bayesian statistics? \n",
    "\n",
    "\n",
    "*Point estimates*, to be introduced in the next chapter, in Bayesian inference are computed using expected values. In more analytical Bayesian inference, we would have been required to evaluate complicated expected values represented as multi-dimensional integrals. No longer. If we can sample from the posterior distribution directly, we simply need to evaluate averages. Much easier. If accuracy is a priority, plots like the ones above show how fast you are converging. And if further accuracy is  desired, just take more samples from the posterior. \n",
    "\n",
    "When is enough enough? When can you stop drawing samples from the posterior? That is the practitioners decision, and also dependent on the variance of the samples (recall from above a high variance means the average will converge slower). \n",
    "\n",
    "We also should understand when the Law of Large Numbers fails. As the name implies, and comparing the graphs above for small $N$, the Law is only true for large sample sizes. Without this, the asymptotic result is not reliable. Knowing in what situations the Law fails can give us *confidence in how unconfident we should be*. The next section deals with this issue."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## The Disorder of Small Numbers\n",
    "\n",
    "The Law of Large Numbers is only valid as $N$ gets *infinitely* large: never truly attainable.  While the law is a powerful tool, it is foolhardy to apply it liberally. Our next example illustrates this.\n",
    "\n",
    "\n",
    "##### Example: Aggregated geographic data\n",
    "\n",
    "\n",
    "Often data comes in aggregated form. For instance, data may be grouped by state, county, or city level. Of course, the population numbers vary per geographic area. If the data is an average of some characteristic of each the geographic areas, we must be conscious of the Law of Large Numbers and how it can *fail* for areas with small populations.\n",
    "\n",
    "We will observe this on a toy dataset. Suppose there are five thousand counties in our dataset. Furthermore,  population number in each state are uniformly distributed between 100 and 1500. The way the population numbers are generated is irrelevant to the discussion, so we do not justify this. We are interested in measuring the average height of individuals per county. Unbeknownst to us, height does **not** vary across county, and each individual, regardless of the county he or she is currently living in, has the same distribution of what their height may be:\n",
    "\n",
    "$$ \\text{height} \\sim \\text{Normal}(150, 15 ) $$\n",
    "\n",
    "We aggregate the individuals at the county level, so we only have data for the *average in the county*. What might our dataset look like?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
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YM+3tfGPFyLuwMjCsNsY8JlYKOjHG+O2RgG8ZYz5tn/M74BfGmNUicilwmjFmiYj8Blhv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\noua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cyWHfichtP8eo27mymerPdiZC0ViQannJ0ef3+33OCg/07toqSFdGQRAE4f6mX4f+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\niIg+9n+bM5it2p4WWwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa44c2bc908>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "figsize( 12.5, 4) \n",
    "std_height = 15\n",
    "mean_height = 150\n",
    "\n",
    "n_counties = 5000\n",
    "pop_generator = np.random.randint\n",
    "norm = np.random.normal\n",
    "\n",
    "#generate some artificial population numbers\n",
    "population = pop_generator(100, 1500, n_counties )\n",
    "\n",
    "average_across_county = np.zeros( n_counties )\n",
    "for i in range( n_counties ):\n",
    "    #generate some individuals and take the mean\n",
    "    average_across_county[i] = norm(mean_height, 1./std_height,\n",
    "                                        population[i] ).mean()\n",
    "    \n",
    "#located the counties with the apparently most extreme average heights.\n",
    "i_min = np.argmin( average_across_county )\n",
    "i_max = np.argmax( average_across_county )\n",
    "\n",
    "#plot population size vs. recorded average\n",
    "plt.scatter( population, average_across_county, alpha = 0.5, c=\"#7A68A6\")\n",
    "plt.scatter( [ population[i_min], population[i_max] ], \n",
    "           [average_across_county[i_min], average_across_county[i_max] ],\n",
    "           s = 60, marker = \"o\", facecolors = \"none\",\n",
    "           edgecolors = \"#A60628\", linewidths = 1.5, \n",
    "            label=\"extreme heights\")\n",
    "\n",
    "plt.xlim( 100, 1500 )\n",
    "plt.title( \"Average height vs. County Population\")\n",
    "plt.xlabel(\"County Population\")\n",
    "plt.ylabel(\"Average height in county\")\n",
    "plt.plot( [100, 1500], [150, 150], color = \"k\", label = \"true expected \\\n",
    "height\", ls=\"--\" )\n",
    "plt.legend(scatterpoints = 1);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "What do we observe? *Without accounting for population sizes* we run the risk of making an enormous inference error: if we ignored population size, we would say that the county with the shortest and tallest individuals have been correctly circled. But this inference is wrong for the following reason. These two counties do *not* necessarily have the most extreme heights. The error results from the calculated average of smaller populations not being a good reflection of the true expected value of the population (which in truth should be $\\mu =150$). The sample size/population size/$N$, whatever you wish to call it,  is simply too small to invoke the Law of Large Numbers effectively. \n",
    "\n",
    "We provide more damning evidence against this inference. Recall the population numbers were uniformly distributed over 100 to 1500. Our intuition should tell us that the counties with the most extreme population heights should also be uniformly spread over 100 to 1500, and certainly independent of the county's population. Not so. Below are the population sizes of the counties with the most extreme heights."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Population sizes of 10 'shortest' counties: \n",
      "[109 135 135 133 109 157 175 120 105 131] \n",
      "\n",
      "Population sizes of 10 'tallest' counties: \n",
      "[122 133 313 109 124 280 106 198 326 216]\n"
     ]
    }
   ],
   "source": [
    "print(\"Population sizes of 10 'shortest' counties: \")\n",
    "print(population[ np.argsort( average_across_county )[:10] ], '\\n')\n",
    "print(\"Population sizes of 10 'tallest' counties: \")\n",
    "print(population[ np.argsort( -average_across_county )[:10] ])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Not at all uniform over 100 to 1500. This is an absolute failure of the Law of Large Numbers. \n",
    "\n",
    "##### Example:  Kaggle's *U.S. Census Return Rate Challenge*\n",
    "\n",
    "Below is data from the 2010 US census, which partitions populations beyond counties to the level of block groups (which are aggregates of city blocks or equivalents). The dataset is from a Kaggle machine learning competition some colleagues and I participated in. The objective was to predict the census letter mail-back rate of a group block, measured between 0 and 100, using census variables (median income, number of females in the block-group, number of trailer parks, average number of children etc.). Below we plot the census mail-back rate versus block group population:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
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Vi+dbDIViPxakET+TN2GqlzubLjBbHPLE9qVimUK+PO7lPtC9c+lzun4iUT8r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O7eyCr6aQqF0gJWXmfzbpfeNG11frnMeOjhyAKzC0UESaQxl6e9P2I88JFkAfLZ9e1sDqnP5tnf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\n4w7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SiIiIhFCOPvFP5P0+BvxSmcZWNSvVlq+HmbOtZwvpiwu0tDbS198OGLMzc8ylF3P3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\nwWgSLyIiIiKywfz/x4zZNvAVNBcAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa4501d8f60>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "figsize( 12.5, 6.5 )\n",
    "data = np.genfromtxt( \"./data/census_data.csv\", skip_header=1, \n",
    "                        delimiter= \",\")\n",
    "plt.scatter( data[:,1], data[:,0], alpha = 0.5, c=\"#7A68A6\")\n",
    "plt.title(\"Census mail-back rate vs Population\")\n",
    "plt.ylabel(\"Mail-back rate\")\n",
    "plt.xlabel(\"population of block-group\")\n",
    "plt.xlim(-100, 15e3 )\n",
    "plt.ylim( -5, 105)\n",
    "\n",
    "i_min = np.argmin(  data[:,0] )\n",
    "i_max = np.argmax(  data[:,0] )\n",
    " \n",
    "plt.scatter( [ data[i_min,1], data[i_max, 1] ], \n",
    "             [ data[i_min,0],  data[i_max,0] ],\n",
    "             s = 60, marker = \"o\", facecolors = \"none\",\n",
    "             edgecolors = \"#A60628\", linewidths = 1.5, \n",
    "             label=\"most extreme points\")\n",
    "\n",
    "plt.legend(scatterpoints = 1);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The above is a classic phenomenon in statistics. I say *classic* referring to the \"shape\" of the scatter plot above. It follows a classic triangular form, that tightens as we increase the sample size (as the Law of Large Numbers becomes more exact). \n",
    "\n",
    "I am perhaps overstressing the point and maybe I should have titled the book *\"You don't have big data problems!\"*, but here again is an example of the trouble with *small datasets*, not big ones. Simply, small datasets cannot be processed using the Law of Large Numbers. Compare with applying the Law without hassle to big datasets (ex. big data). I mentioned earlier that paradoxically big data prediction problems are solved by relatively simple algorithms. The paradox is partially resolved by understanding that the Law of Large Numbers creates solutions that are *stable*, i.e. adding or subtracting a few data points will not affect the solution much. On the other hand, adding or removing data points to a small dataset can create very different results. \n",
    "\n",
    "For further reading on the hidden dangers of the Law of Large Numbers, I would highly recommend the excellent manuscript [The Most Dangerous Equation](http://nsm.uh.edu/~dgraur/niv/TheMostDangerousEquation.pdf). "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Example: How to order Reddit submissions\n",
    "\n",
    "You may have disagreed with the original statement that the Law of Large numbers is known to everyone, but only implicitly in our subconscious decision making. Consider ratings on online products: how often do you trust an average 5-star rating if there is only 1 reviewer? 2 reviewers? 3 reviewers? We implicitly understand that with such few reviewers that the average rating is **not** a good reflection of the true value of the product.\n",
    "\n",
    "This has created flaws in how we sort items, and more generally, how we compare items. Many people have realized that sorting online search results by their rating, whether the objects be books, videos, or online comments, return poor results. Often the seemingly top videos or comments have perfect ratings only from a few enthusiastic fans, and truly more quality videos or comments are hidden in later pages with *falsely-substandard* ratings of around 4.8. How can we correct this?\n",
    "\n",
    "Consider the popular site Reddit (I purposefully did not link to the website as you would never come back). The site hosts links to stories or images, called submissions, for people to comment on. Redditors can vote up or down on each submission (called upvotes and downvotes). Reddit, by default, will sort submissions to a given subreddit by Hot, that is, the submissions that have the most upvotes recently.\n",
    "\n",
    "<img src=\"http://i.imgur.com/3v6bz9f.png\" />\n",
    "\n",
    "\n",
    "How would you determine which submissions are the best? There are a number of ways to achieve this:\n",
    "\n",
    "1. *Popularity*: A submission is considered good if it has many upvotes. A problem with this model is that a submission with hundreds of upvotes, but thousands of downvotes. While being very *popular*, the submission is likely more controversial than best.\n",
    "2. *Difference*: Using the *difference* of upvotes and downvotes. This solves the above problem, but fails when we consider the temporal nature of submission. Depending on when a submission is posted, the website may be experiencing high or low traffic. The difference method will bias the *Top* submissions to be the those made during high traffic periods, which have accumulated more upvotes than submissions that were not so graced, but are not necessarily the best.\n",
    "3. *Time adjusted*:  Consider using Difference divided by the age of the submission. This creates a *rate*, something like *difference per second*, or *per minute*. An immediate counter-example is, if we use per second, a 1 second old submission with 1 upvote would be better than a 100 second old submission with 99 upvotes. One can avoid this by only considering at least t second old submission. But what is a good t value? Does this mean no submission younger than t is good? We end up comparing unstable quantities with stable quantities (young vs. old submissions).\n",
    "3. *Ratio*: Rank submissions by the ratio of upvotes to total number of votes (upvotes plus downvotes). This solves the temporal issue, such that new submissions who score well can be considered Top just as likely as older submissions, provided they have many upvotes to total votes. The problem here is that a submission with a single upvote (ratio = 1.0) will beat a submission with 999 upvotes and 1 downvote (ratio = 0.999), but clearly the latter submission is *more likely* to be better.\n",
    "\n",
    "I used the phrase *more likely* for good reason. It is possible that the former submission, with a single upvote, is in fact a better submission than the later with 999 upvotes. The hesitation to agree with this is because we have not seen the other 999 potential votes the former submission might get. Perhaps it will achieve an additional 999 upvotes and 0 downvotes and be considered better than the latter, though not likely.\n",
    "\n",
    "What we really want is an estimate of the *true upvote ratio*. Note that the true upvote ratio is not the same as the observed upvote ratio: the true upvote ratio is hidden, and we only observe upvotes vs. downvotes (one can think of the true upvote ratio as \"what is the underlying probability someone gives this submission a upvote, versus a downvote\"). So the 999 upvote/1 downvote submission probably has a true upvote ratio close to 1, which we can assert with confidence thanks to the Law of Large Numbers, but on the other hand we are much less certain about the true upvote ratio of the submission with only a single upvote. Sounds like a Bayesian problem to me.\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "One way to determine a prior on the upvote ratio is to look at the historical distribution of upvote ratios. This can be accomplished by scraping Reddit's submissions and determining a distribution. There are a few problems with this technique though:\n",
    "\n",
    "1. Skewed data:  The vast majority of submissions have very few votes, hence there will be many submissions with ratios near the extremes (see the \"triangular plot\" in the above Kaggle dataset), effectively skewing our distribution to the extremes. One could try to only use submissions with votes greater than some threshold. Again, problems are encountered. There is a tradeoff between number of submissions available to use and a higher threshold with associated ratio precision. \n",
    "2. Biased data: Reddit is composed of different subpages, called subreddits. Two examples are *r/aww*, which posts pics of cute animals, and *r/politics*. It is very likely that the user behaviour towards submissions of these two subreddits are very different: visitors are likely friendly and affectionate in the former, and would therefore upvote submissions more, compared to the latter, where submissions are likely to be controversial and disagreed upon. Therefore not all submissions are the same. \n",
    "\n",
    "\n",
    "In light of these, I think it is better to use a `Uniform` prior.\n",
    "\n",
    "\n",
    "With our prior in place, we can find the posterior of the true upvote ratio. The Python script `top_showerthoughts_submissions.py` will scrape the best posts from the `showerthoughts` community on Reddit. This is a text-only community so the title of each post *is* the post. Below is the top post as well as some other sample posts:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Post contents: \n",
      "\n",
      "Toilet paper should be free and have advertising printed on it.\n"
     ]
    }
   ],
   "source": [
    "#adding a number to the end of the %run call will get the ith top post.\n",
    "%run top_showerthoughts_submissions.py 2\n",
    "\n",
    "print(\"Post contents: \\n\")\n",
    "print(top_post)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Some Submissions (out of 98 total) \n",
      "-----------\n",
      "\"Rappers from the 90's used guns when they had beef rappers today use Twitter.\"\n",
      "upvotes/downvotes:  [32  3] \n",
      "\n",
      "\"All polls are biased towards people who are willing to take polls\"\n",
      "upvotes/downvotes:  [1918  101] \n",
      "\n",
      "\"Taco Bell should give customers an extra tortilla so they can make a burrito out of all the stuff that spilled out of the other burritos they ate.\"\n",
      "upvotes/downvotes:  [79 17] \n",
      "\n",
      "\"There should be an /r/alanismorissette where it's just examples of people using \"ironic\" incorrectly\"\n",
      "upvotes/downvotes:  [33  6] \n",
      "\n"
     ]
    }
   ],
   "source": [
    "\"\"\"\n",
    "contents: an array of the text from the last 100 top submissions to a subreddit\n",
    "votes: a 2d numpy array of upvotes, downvotes for each submission.\n",
    "\"\"\"\n",
    "n_submissions = len(votes)\n",
    "submissions = np.random.randint( n_submissions, size=4)\n",
    "print(\"Some Submissions (out of %d total) \\n-----------\"%n_submissions)\n",
    "for i in submissions:\n",
    "    print('\"' + contents[i] + '\"')\n",
    "    print(\"upvotes/downvotes: \",votes[i,:], \"\\n\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " For a given true upvote ratio $p$ and $N$ votes, the number of upvotes will look like a Binomial random variable with parameters $p$ and $N$. (This is because of the equivalence between upvote ratio and probability of upvoting versus downvoting, out of $N$ possible votes/trials). We create a function that performs Bayesian inference on $p$, for a particular submission's upvote/downvote pair."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [],
   "source": [
    "import pymc3 as pm\n",
    "\n",
    "def posterior_upvote_ratio( upvotes, downvotes, samples = 20000):\n",
    "    \"\"\"\n",
    "    This function accepts the number of upvotes and downvotes a particular submission recieved, \n",
    "    and the number of posterior samples to return to the user. Assumes a uniform prior.\n",
    "    \"\"\"\n",
    "    N = upvotes + downvotes\n",
    "    with pm.Model() as model:\n",
    "        upvote_ratio = pm.Uniform(\"upvote_ratio\", 0, 1)\n",
    "        observations = pm.Binomial( \"obs\",  N, upvote_ratio, observed=upvotes)\n",
    "        \n",
    "        trace = pm.sample(samples, step=pm.Metropolis())\n",
    "    \n",
    "    burned_trace = trace[int(samples/4):]\n",
    "    return burned_trace[\"upvote_ratio\"]\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Below are the resulting posterior distributions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Applied interval-transform to upvote_ratio and added transformed upvote_ratio_interval_ to model.\n",
      " [-------100%-------] 20000 of 20000 in 1.4 sec. | SPS: 14595.5 | ETA: 0.0Applied interval-transform to upvote_ratio and added transformed upvote_ratio_interval_ to model.\n",
      " [-------100%-------] 20000 of 20000 in 1.3 sec. | SPS: 15189.5 | ETA: 0.0Applied interval-transform to upvote_ratio and added transformed upvote_ratio_interval_ to model.\n",
      " [-------100%-------] 20000 of 20000 in 1.3 sec. | SPS: 15429.0 | ETA: 0.0Applied interval-transform to upvote_ratio and added transformed upvote_ratio_interval_ to model.\n",
      " [-------100%-------] 20000 of 20000 in 1.3 sec. | SPS: 15146.5 | ETA: 0.0"
     ]
    },
    {
     "data": {
      "image/png": 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biGr7c0FfBHOi6cl4BXgZ1fbuX9QtO47z4JtR90C1f/xbF9c21BV4JtNHnY6qgdqpnkE1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\nKCIiIiJlKWEUERERkbL+Hwj/zthEET8YAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa4232cbb38>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "figsize( 11., 8)\n",
    "posteriors = []\n",
    "colours = [\"#348ABD\", \"#A60628\", \"#7A68A6\", \"#467821\", \"#CF4457\"]\n",
    "for i in range(len(submissions)):\n",
    "    j = submissions[i]\n",
    "    posteriors.append( posterior_upvote_ratio( votes[j, 0], votes[j,1] ) )\n",
    "    plt.hist( posteriors[i], bins = 10, normed = True, alpha = .9, \n",
    "            histtype=\"step\",color = colours[i%5], lw = 3,\n",
    "            label = '(%d up:%d down)\\n%s...'%(votes[j, 0], votes[j,1], contents[j][:50]) )\n",
    "    plt.hist( posteriors[i], bins = 10, normed = True, alpha = .2, \n",
    "            histtype=\"stepfilled\",color = colours[i], lw = 3, )\n",
    "    \n",
    "plt.legend(loc=\"upper left\")\n",
    "plt.xlim( 0, 1)\n",
    "plt.title(\"Posterior distributions of upvote ratios on different submissions\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Some distributions are very tight, others have very long tails (relatively speaking), expressing our uncertainty with what the true upvote ratio might be.\n",
    "\n",
    "### Sorting!\n",
    "\n",
    "We have been ignoring the goal of this exercise: how do we sort the submissions from *best to worst*? Of course, we cannot sort distributions, we must sort scalar numbers. There are many ways to distill a distribution down to a scalar: expressing the distribution through its expected value, or mean, is one way. Choosing the mean is a bad choice though. This is because the mean does not take into account the uncertainty of distributions.\n",
    "\n",
    "I  suggest using the *95% least plausible value*, defined as the value such that there is only a 5% chance the true parameter is lower (think of the lower bound on the 95% credible region). Below are the posterior distributions with the 95% least-plausible value plotted:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1 0 2 3] [0.80034320917496615, 0.94092009444598201, 0.74660503350561902, 0.72190353389632911]\n"
     ]
    },
    {
     "data": {
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45JNPTMoSHh5O48aNAXBxcaFHjx6AOgT2ww8/4OXlpe10c/ToUdq0aYOLiwtt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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa42355f9b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "N = posteriors[0].shape[0]\n",
    "lower_limits = []\n",
    "\n",
    "for i in range(len(submissions)):\n",
    "    j = submissions[i]\n",
    "    plt.hist( posteriors[i], bins = 20, normed = True, alpha = .9, \n",
    "            histtype=\"step\",color = colours[i], lw = 3,\n",
    "            label = '(%d up:%d down)\\n%s...'%(votes[j, 0], votes[j,1], contents[j][:50]) )\n",
    "    plt.hist( posteriors[i], bins = 20, normed = True, alpha = .2, \n",
    "            histtype=\"stepfilled\",color = colours[i], lw = 3, )\n",
    "    v = np.sort( posteriors[i] )[ int(0.05*N) ]\n",
    "    #plt.vlines( v, 0, 15 , color = \"k\", alpha = 1, linewidths=3 )\n",
    "    plt.vlines( v, 0, 10 , color = colours[i], linestyles = \"--\",  linewidths=3  )\n",
    "    lower_limits.append(v)\n",
    "    plt.legend(loc=\"upper left\")\n",
    "\n",
    "plt.legend(loc=\"upper left\")\n",
    "plt.title(\"Posterior distributions of upvote ratios on different submissions\");\n",
    "order = np.argsort( -np.array( lower_limits ) )\n",
    "print(order, lower_limits)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The best submissions, according to our procedure, are the submissions that are *most-likely* to score a high percentage of upvotes. Visually those are the submissions with the 95% least plausible value close to 1.\n",
    "\n",
    "Why is sorting based on this quantity a good idea? By ordering by the 95% least plausible value, we are being the most conservative with what we think is best.  When using the lower-bound of the 95% credible interval, we believe with high certainty that the 'true upvote ratio' is at the very least equal to this value (or greater), thereby ensuring that the best submissions are still on top. Under this ordering, we impose the following very natural properties:\n",
    "\n",
    "1. given two submissions with the same observed upvote ratio, we will assign the submission with more votes as better (since we are more confident it has a higher ratio).\n",
    "2. given two submissions with the same number of votes, we still assign the submission with more upvotes as *better*.\n",
    "\n",
    "### But this is too slow for real-time!\n",
    "\n",
    "I agree, computing the posterior of every submission takes a long time, and by the time you have computed it, likely the data has changed. I delay the mathematics to the appendix, but I suggest using the following formula to compute the lower bound very fast.\n",
    "\n",
    "$$ \\frac{a}{a + b} - 1.65\\sqrt{ \\frac{ab}{ (a+b)^2(a + b +1 ) } }$$\n",
    "\n",
    "where \n",
    "\\begin{align}\n",
    "& a = 1 + u \\\\\\\\\n",
    "& b = 1 + d \\\\\\\\\n",
    "\\end{align}\n",
    "\n",
    "$u$ is the number of upvotes, and $d$ is the number of downvotes. The formula is a shortcut in Bayesian inference, which will be further explained in Chapter 6 when we discuss priors in more detail.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Approximate lower bounds:\n",
      "[ 0.93349005  0.9532194   0.94149718  0.90859764  0.88705356  0.8558795\n",
      "  0.85644927  0.93752679  0.95767101  0.91131012  0.910073    0.915999\n",
      "  0.9140058   0.83276025  0.87593961  0.87436674  0.92830849  0.90642832\n",
      "  0.89187973  0.89950891  0.91295322  0.78607629  0.90250203  0.79950031\n",
      "  0.85219422  0.83703439  0.7619808   0.81301134  0.7313114   0.79137561\n",
      "  0.82701445  0.85542404  0.82309334  0.75211374  0.82934814  0.82674958\n",
      "  0.80933194  0.87448152  0.85350205  0.75460106  0.82934814  0.74417233\n",
      "  0.79924258  0.8189683   0.75460106  0.90744016  0.83838023  0.78802791\n",
      "  0.78400654  0.64638659  0.62047936  0.76137738  0.81365241  0.83838023\n",
      "  0.78457533  0.84980627  0.79249393  0.69020315  0.69593922  0.70758151\n",
      "  0.70268831  0.91620627  0.73346864  0.86382644  0.80877728  0.72708753\n",
      "  0.79822085  0.68333632  0.81699014  0.65100453  0.79809005  0.74702492\n",
      "  0.77318569  0.83221179  0.66500492  0.68134548  0.7249286   0.59412132\n",
      "  0.58191312  0.73142963  0.73142963  0.66251028  0.87152685  0.74107856\n",
      "  0.60935684  0.87152685  0.77484517  0.88783675  0.81814153  0.54569789\n",
      "  0.6122496   0.75613569  0.53511973  0.74556767  0.81814153  0.85773646\n",
      "  0.6122496   0.64814153]\n",
      "\n",
      "\n",
      "Top 40 Sorted according to approximate lower bounds:\n",
      "\n",
      "\n",
      "596 18 Someone should develop an AI specifically for reading Terms & Conditions and flagging dubious parts.\n",
      "-------------\n",
      "2360 98 Porn is the only industry where it is not only acceptable but standard to separate people based on race, sex and sexual preference.\n",
      "-------------\n",
      "1918 101 All polls are biased towards people who are willing to take polls\n",
      "-------------\n",
      "948 50 They should charge less for drinks in the drive-thru because you can't refill them.\n",
      "-------------\n",
      "3740 239 When I was in elementary school and going through the DARE program, I was positive a gang of older kids was going to corner me and force me to smoke pot. Then I became an adult and realized nobody is giving free drugs to somebody that doesn't want them.\n",
      "-------------\n",
      "166 7 \"Noted\" is the professional way of saying \"K\".\n",
      "-------------\n",
      "29 0 Rewatching Mr. Bean, I've realised that the character is an eccentric genius and not a blithering idiot.\n",
      "-------------\n",
      "289 18 You've been doing weird cameos in your friends' dreams since kindergarten.\n",
      "-------------\n",
      "269 17 At some point every parent has stopped wiping their child's butt and hoped for the best.\n",
      "-------------\n",
      "121 6 Is it really fair to say a person over 85 has heart failure? Technically, that heart has done exceptionally well.\n",
      "-------------\n",
      "535 40 It's surreal to think that the sun and moon and stars we gaze up at are the same objects that have been observed for millenia, by everyone in the history of humanity from cavemen to Aristotle to Jesus to George Washington.\n",
      "-------------\n",
      "527 40 I wonder if America's internet is censored in a similar way that North Korea's is, but we have no idea of it happening.\n",
      "-------------\n",
      "1510 131 Kenny's family is poor because they're always paying for his funeral.\n",
      "-------------\n",
      "43 1 If I was as careful with my whole paycheck as I am with my last $20 I'd be a whole lot better off\n",
      "-------------\n",
      "162 10 Black hair ties are probably the most popular bracelets in the world.\n",
      "-------------\n",
      "107 6 The best answer to the interview question \"What is your greatest weakness?\" is \"interviews\".\n",
      "-------------\n",
      "127 8 Surfing the internet without ads feels like a summer evening without mosquitoes\n",
      "-------------\n",
      "159 12 I wonder if Superman ever put a pair of glasses on Lois Lane's dog, and she was like \"what's this Clark? Did you get me a new dog?\"\n",
      "-------------\n",
      "21 0 Sitting on a cold toilet seat or a warm toilet seat both suck for different reasons.\n",
      "-------------\n",
      "1414 157 My life is really like Rihanna's song, \"just work work work work work\" and the rest of it I can't really understand.\n",
      "-------------\n",
      "222 22 I'm honestly slightly concerned how often Reddit commenters make me laugh compared to my real life friends.\n",
      "-------------\n",
      "52 3 The world must have been a spookier place altogether when candles and gas lamps were the only sources of light at night besides the moon and the stars.\n",
      "-------------\n",
      "194 19 I have not been thankful enough in the last few years that the Black Eyed Peas are no longer ever on the radio\n",
      "-------------\n",
      "18 0 Living on the coast is having the window seat of the land you live on.\n",
      "-------------\n",
      "18 0 Binoculars are like walkie talkies for the deaf.\n",
      "-------------\n",
      "28 1 Now that I am a parent of multiple children I have realized that my parents were lying through their teeth when they said they didn't have a favorite.\n",
      "-------------\n",
      "16 0 I sneer at people who read tabloids, but every time I look someone up on Wikipedia the first thing I look for is what controversies they've been involved in.\n",
      "-------------\n",
      "1559 233 Kid's menus at restaurants should be smaller portions of the same adult dishes at lower prices and not the junk food that they usually offer.\n",
      "-------------\n",
      "1426 213 Eventually once all phones are waterproof we'll be able to push people into pools again\n",
      "-------------\n",
      "61 5 Myspace is so outdated that jokes about it being outdated has become outdated\n",
      "-------------\n",
      "52 4 As a kid, seeing someone step on a banana peel and not slip was a disappointment.\n",
      "-------------\n",
      "90 9 Yahoo!® is the RadioShack® of the Internet.\n",
      "-------------\n",
      "34 2 People who \"tell it like it is\" rarely do so to say something nice\n",
      "-------------\n",
      "39 3 Closing your eyes after turning off your alarm is a very dangerous game.\n",
      "-------------\n",
      "39 3 Your known 'first word' is the first word your parents heard you speak. In reality, it may have been a completely different word you said when you were alone.\n",
      "-------------\n",
      "87 10 \"Smells Like Teen Spirit\" is as old to listeners of today as \"Yellow Submarine\" was to listeners of 1991.\n",
      "-------------\n",
      "239 36 if an ocean didnt stop immigrants from coming to America what makes us think a wall will?\n",
      "-------------\n",
      "22 1 The phonebook was the biggest invasion of privacy that everyone was oddly ok with.\n",
      "-------------\n",
      "57 6 I'm actually the most productive when I procrastinate because I'm doing everything I possibly can to avoid the main task at hand.\n",
      "-------------\n",
      "57 6 You will never feel how long time is until you have allergies and snot slowly dripping out of your nostrils, while sitting in a classroom with no tissues.\n",
      "-------------\n"
     ]
    }
   ],
   "source": [
    "def intervals(u,d):\n",
    "    a = 1. + u\n",
    "    b = 1. + d\n",
    "    mu = a/(a+b)\n",
    "    std_err = 1.65*np.sqrt( (a*b)/( (a+b)**2*(a+b+1.) ) )\n",
    "    return ( mu, std_err )\n",
    "\n",
    "print(\"Approximate lower bounds:\")\n",
    "posterior_mean, std_err  = intervals(votes[:,0],votes[:,1])\n",
    "lb = posterior_mean - std_err\n",
    "print(lb)\n",
    "print(\"\\n\")\n",
    "print(\"Top 40 Sorted according to approximate lower bounds:\")\n",
    "print(\"\\n\")\n",
    "order = np.argsort( -lb )\n",
    "ordered_contents = []\n",
    "for i in order[:40]:\n",
    "    ordered_contents.append( contents[i] )\n",
    "    print(votes[i,0], votes[i,1], contents[i])\n",
    "    print(\"-------------\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can view the ordering visually by plotting the posterior mean and bounds, and sorting by the lower bound. In the plot below, notice that the left error-bar is sorted (as we suggested this is the best way to determine an ordering), so the means, indicated by dots, do not follow any strong pattern. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
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ETeKpBMX5vL6JdVcDG4ALgO+Y2Ws5/LKUoHr/HrAvQe3+YjM7SlIxie9Y0jBCNOxm4DUz\n65hjvM3WnY3rlDiO4ziOs7MxvKycAiupjZRAUG1fo4WMGn19s9m1LTol2+X4VhYzgbMltY+/kP9X\nrMvVfosWJamTmc01s3LCRucLWU3WAns1YqjZwPGSvhjH3UPSwckGZrYGeDeRL/ItYHo+0/LU3ws8\nQNjMbOnuMDPmDMKxs10kfRb4SqLNCuDI+Pmc2o7BT6+a2WhgLnAYwTebbSBzMBG4knACrc6GJDKb\ncGRtBlABXMWm7zdpdy1mthZYIenchI1ds9s5juM4juM4MHjIQKqWT2b9hnVA2JBULZ/M4CEDm9my\nrSftTUmdF20zWwj8mvAy/BJwt5ktytN+S1/Uf66QPF8FvGhmVVnPq4CNMcn7inzzmdm/CPkTD0la\nBMwCDs0xX39gjKRKQm7KDVu4jqcJkYJfN7Swemx9AngdeDWOMyvR5gbgVklzCBGLDFcqXAhQCawH\nJhN880k9vsHMVgNLqf+o2UxCPs1yYAHhKNyMxPN8vrgYGKhwacArbMoJqvffgOeUpEt2uNtpOty3\n6eL+TRf3b3q4b9Oltfi3qLiI0eOuZY0W8ubbf2KNFjJ63LU79O1bqR7fcupHUikw1sz6NLctjUHh\nBrJFhGNxa5vbHoCxY8fagAEDGm7obBUVFRW150edpsV9my7u33Rx/6aH+zZd3L/psi3Ht3xT0kxI\n+jFBt+WbZvZSQ+2bG0l9CcfNxprZdhOcbAjPKXEcx3Ecx2kZbMumZNemNsZpHGZ2EzuQ7oaZTQUO\nbG47HMdxHMdxnNbH9hRPRNLZkjZmhPFiXbGCWGJG1K9JFMwlrc0ef3sjqVzS0Bz1n43XFqc1b04/\nKog99ktr3obI549twXNK0qW1nL1tibhv08X9my7u3/Rw36aL+7flsr0jJRcQEqEvBJL3lVmez9tC\nGmMCIGmXHBodjcbMVgHnN6FJOafJUdcdKCUktqeKpDZm9kna8ziO4ziO47Q2aqprGH/Hvax9/yP2\nKtydwUMG7tBJ7I1hu0VK4vW/xwMDCZuSLenbX9KTkqZJWibpfxPPhsabpKrirVH1jdNZ0suSFsRb\nnr6Yo814SXPimOWJ+hWSRkmaB5wrqZOkyZLmSpqejP5k0V3SrGj3d+NYyehQsaQZkubFP8fE+s/E\ncRfEtR0f60+J482T9HBMPkfSaZKWRvu+nmNdbQm3cZ0fxzxP0j6SnpC0KI55RGxbpaDDgqR/Sbo4\nfp4oqW89NveJ9U8RbgND0rVx7TNI3GAm6XJJr8bv4Tc57M37nW/m3O7d87jdaQo8GTA93Lfp4v5N\nF/dverhv02VH8G9NdQ1lQ0dSYCUctN9JFFgJZUNHUlNd09ympcr2jJR8DZhiZq/HF92SeD1wY+kJ\nHA6sA+ZKejbW94/P2gAvS3ohccVwNt8HfmFmD0naNfbJ5hoze0/SLsBUSZPM7JX47F9mVgog6Y/A\npWb2hqSjgDuBXBKWXYCjCfooCxN2ZyIZq4GTzWy9pC8R1Nl7At8k+OtnkgTsoSDE+D9AXzP7SFIZ\nMFTSz4G7gRPNbLmkh7ONMLMN8cU+KRZ5K7DAzP5L0leA+4ESgr7I8ZJqgDcIqvEPEJTZvx9tz2Uz\nsf/hZlYjqQchItQVaEe4InhebPdj4MBoVz59lDrfuZktyNPWcRzHcRxnh2PMNVM2K8+cN4mju51e\nK4zYrm17unbqx2WDRtC7tFZ2jqtuPG272pk223NTciHwi/j5YcJL95ZsSp43s/egVrW8N+Hl+Akz\nWxfrH4/1i8gtWPgScK2kz8d+r+doc4GkQQTffAboDGQ2JQ/HeToAxwGPxg0DQNs8dj9lZuuBtyX9\nCTgq2pehLXCXpO7AJ0BGpHEucG+McDxlZosknRjteTHO2zau6TBgedQGgbCBGJTHniS9iFEVM5sm\naV9JexI2JX2AauCXwCBJnwPeiZuhAuD2HDYDzDGzzFa+N8HPHwMfS3o60W4R8BtJTwJP5rEv+Z0/\nHu3dbFNyyy230KFDB4qKQkizsLCQLl261P4Skjk76uWtK995553uz5TKyXPNLcGe1lZ2/7p/d9Ry\npq6l2NPaypm6lmJPply9cgkAxR07Y2asWr28tgywavVy3l2zunYN1SuXUFGxZ7Pbn/lcUxNe/UpL\nS+nbN9dv9A2zXa4ElrQP8DdCVMAIEQozswMlFQPPmFlXSX2AYWZ2Vlb//oQowCWxfD3wr/h4/6jg\njqQbgNVmdrukNWZWkBw/tjkIOAO4DPiemb2QmOdA4HlCNGGNpAnANDO7T9KKWP+OpL2A18ysYwPr\nLics9PpYngg8RhAqzKy5HOhgZmWS2gAfmVm72P4zwOnAEGAc8B5woZldlDVPN+DWjN6JpDOBQXn8\nmIyUzAfOMbM3Y7mGsOnZm7ABexO4FrgV+CPwBTO7Op/N2d+fwnG6fczsulgeC6w0s3FxU3UCQSSx\nH3BEMk8n33eefR2x65SkS0WF3+eeFu7bdHH/pov7Nz3ct+myI/h3eFk5BVZSGymBoNi+RgsZNfr6\neno2P9tyJfD2yik5D7jPzA4ys05mVgyskJT5V9EY40+RtLek3YGzgReBCuBrktrH6MV/sUk9vM6Y\nkg4ysxXxxfYpwrGiJAXAB8BaSZ8mvCzXIQoHrpB0bmLs7LEyfE1Su3j0qg8hApKkEFgVP3+beKRM\nUhFhg3UvQR+kBzCbcKzqi7HNHpIOBl4DiuOGC/Ln7KyNa8wwk6CkTozC/NPMPjCzvwH7AwfHDUsF\ncBWbfJvT5hzMAM6WtFvcyJ2ZeFZkZtOB4dGmPXP0z/Wdb4bnlKRLS/8P946M+zZd3L/p4v5ND/dt\nuuwI/h08ZCBVyyezfsM6IGxIqpZPZvCQgc1sWbpsr03JN4AnsuoeZ9PLc2PCNXNin0rgUTNbEHNS\nfk140X8JuNvMquoZ83xJr0haSMhVuC/5MPatBJYSjkBVJB9njXURMDAmar9C+MU/F1XAC8As4AYz\neyvr+XjgO9GmQwibIoATgUWSFhDyMm4xs38B3wEekrQojnloPB51KfB7hUT3f+SxZRrQWTHRHbgO\nODKOdSMhPyfDbGBZ/DwT+FzCH9k2f5hrsvj9PBx98DvCd0jM53kgzjs/rm1NjiHqfOd51uU4juM4\njtMqKCouYvS4a1mjhbz59p9Yo4WMHndtq799a4dQdM8+duS0fhr7nfvxrXTZEcLcOyru23Rx/6aL\n+zc93Lfp4v5Nlx3h+JbjOI7jOI7jOE5OdohIiePkY+rUqdajR4/mNsNxHMdxHGenp0VFSuK1sgtj\n3sIqSX+Ln9+NuRepE4X3bmu4ZaPGKpc0NEd9rQBiI8eZFnU7moWm9EmOsXvFXJ0FknZrgvFSs9Vx\nHMdxHMdpeTT5psTM3jGzEjPrQRAUHBc/dwc21t+7aU1pJXMQr91tCtKy9yLgRjPrEZPu66WR66lj\na65+lZWVjbPQ2SqS95A7TYv7Nl3cv+ni/k0P92267Aj+ramuYXhZOUMuLWN4WXmrV3LPkHZOSXb4\nZldJd8df1adkflWX1EnSZElzJU2XdIikPSUtz7yIStorWa6dQDpP0uIYnXkh8ahjHHOZpJsS7S+U\nVBX/jErUr018PidqlGy+GOnIeNvWQoJ2SO5FSz+O4y+UdGPi0fmSXpb0mqTjY9tiSTMkzYt/jon1\nfWL9U8Crse4nse8MSb/JRHBy+S+fbbH9/pIei7a8LOnYxJyZKNd8SR0kfSaOuSCu6fissQYSbgcb\nIen+WPfz+J0sknR+vvVkjXNJ/K5mA8cn6idIujPW35Tdz3Ecx3Ecp7VQU11D2dCRFFgJB+13EgVW\nQtnQkTvFxmTX7TzfwcA3zOx7kh4GzgF+A9wNXGpmb0g6CrjTzPpKmkYQD3wauACYZGafZI35E+BU\nM1uloDSeoRshOrMBWCbpVkKkZhRQQhAifF7SWWb2NHV/mc8VVfg/YLCZvShpdK4FSjqNoMfR08w+\nlrR34nEbMztaUj/CdbynEK7vPdnM1kv6EvAQ0DO2LwEON7MaSaUEHZYuwG4EZfN5sV0d/wH1yWne\nQohgzZL0BeAPBNHEYXF9L0naA8hcNTzFzH4mScAemznJ7F4FvZlnzOxxSV8HuppZF0kHAHMlTc9e\nT5bPPhP9UQKsIVyhnLz+t6OZHZNrIa5Tki5+Q0l6uG/Txf2bLu7f9HDfpktL8O+Ya6bkfTZz3iSO\n7nZ6rXBiu7bt6dqpH5cNGkHv0nNy9rnqxtNSsXN7s703JcvNLJOHMR84UEH08Djg0fjSC9A2/n0v\ncDVhU3IJ8N0cY1YAEyU9QtC0yDDVzD4AkPQqUEwQBJxmZu/E+gcJquJP04CAo6RCoNDMMgJ+9wO5\n/hWcDEzIHGMys/cSzzL2zY/2ALQDbpfUHfiEsHHLMCfxAn888JSZbQA2SHom2lWf//JxMvDlRPs9\n4ybkReDm6JfHzWylpLnAvZLaxvkXNTB2L8LGCjNbHaNXPQnCjXOyNySRo9n8e3k4yw+P5pvsscce\n45577qGoKNzdXVhYSJcuXWr/o5MJ03rZy172spe97GUvt5Ry9colABR37LxZ2cxo17b9Zs/btW3P\nu2tWU71ySZ32mXJzrSfzuaYmvN6VlpbSt299v4vnJ9XbtySVA2vNbJykYsKv6V3js2FAB+Bm4DUz\n65hnjIXAlcBN+X4tl9QTOIOgLt6DIGRYq3ERX+B/DuwNnGNm/WP9AKCzmV0laY2ZFcT6i4C+ZjYg\nswbCBqkqqtEjqQvwYGY9CVvGAEujEnuyfhowzMwWKKi7zzWzTnH8DmZWpnA07SMzayepT2x/Vux/\nBbC3mV0fy2OBlcC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4D7jNzPrEticRfsU/V9IKQnTiHUlnABea2UVZY3cDbk30PxMYZGa5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\ns9B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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa42332e438>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "r_order = order[::-1][-40:]\n",
    "plt.errorbar( posterior_mean[r_order], np.arange( len(r_order) ), \n",
    "               xerr=std_err[r_order], capsize=0, fmt=\"o\",\n",
    "                color = \"#7A68A6\")\n",
    "plt.xlim( 0.3, 1)\n",
    "plt.yticks( np.arange( len(r_order)-1,-1,-1 ), map( lambda x: x[:30].replace(\"\\n\",\"\"), ordered_contents) );"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the graphic above, you can see why sorting by mean would be sub-optimal."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Extension to Starred rating systems\n",
    "\n",
    "The above procedure works well for upvote-downvotes schemes, but what about systems that use star ratings, e.g. 5 star rating systems. Similar problems apply with simply taking the average: an item with two perfect ratings would beat an item with thousands of perfect ratings, but a single sub-perfect rating. \n",
    "\n",
    "\n",
    "We can consider the upvote-downvote problem above as binary: 0 is a downvote, 1 if an upvote. A $N$-star rating system can be seen as a more continuous version of above, and we can set $n$ stars rewarded is equivalent to rewarding $\\frac{n}{N}$. For example, in a 5-star system, a 2 star rating corresponds to 0.4. A perfect rating is a 1. We can use the same formula as before, but with $a,b$ defined differently:\n",
    "\n",
    "\n",
    "$$ \\frac{a}{a + b} - 1.65\\sqrt{ \\frac{ab}{ (a+b)^2(a + b +1 ) } }$$\n",
    "\n",
    "where \n",
    "\n",
    "\\begin{align}\n",
    "& a = 1 + S \\\\\\\\\n",
    "& b = 1 + N - S \\\\\\\\\n",
    "\\end{align}\n",
    "\n",
    "where $N$ is the number of users who rated, and $S$ is the sum of all the ratings, under the equivalence scheme mentioned above. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Example: Counting Github stars\n",
    "\n",
    "What is the average number of stars a Github repository has? How would you calculate this? There are over 6 million respositories, so there is more than enough data to invoke the Law of Large numbers. Let's start pulling some data. TODO"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Conclusion\n",
    "\n",
    "While the Law of Large Numbers is cool, it is only true so much as its name implies: with large sample sizes only. We have seen how our inference can be affected by not considering *how the data is shaped*. \n",
    "\n",
    "1. By (cheaply) drawing many samples from the posterior distributions, we can ensure that the Law of Large Number applies as we approximate expected values (which we will do in the next chapter).\n",
    "\n",
    "2. Bayesian inference understands that with small sample sizes, we can observe wild randomness. Our posterior distribution will reflect this by being more spread rather than tightly concentrated. Thus, our inference should be correctable.\n",
    "\n",
    "3. There are major implications of not considering the sample size, and trying to sort objects that are unstable leads to pathological orderings. The method provided above solves this problem.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Appendix\n",
    "\n",
    "##### Derivation of sorting submissions formula\n",
    "\n",
    "Basically what we are doing is using a Beta prior (with parameters $a=1, b=1$, which is a uniform distribution), and using a Binomial likelihood with observations $u, N = u+d$. This means our posterior is a Beta distribution with parameters $a' = 1 + u, b' = 1 + (N - u) = 1+d$. We then need to find the value, $x$, such that 0.05 probability is less than $x$. This is usually done by inverting the CDF ([Cumulative Distribution Function](http://en.wikipedia.org/wiki/Cumulative_Distribution_Function)), but the CDF of the beta, for integer parameters, is known but is a large sum [3]. \n",
    "\n",
    "We instead use a Normal approximation. The mean of the Beta is $\\mu = a'/(a'+b')$ and the variance is \n",
    "\n",
    "$$\\sigma^2 = \\frac{a'b'}{ (a' + b')^2(a'+b'+1) }$$\n",
    "\n",
    "Hence we solve the following equation for $x$ and have an approximate lower bound. \n",
    "\n",
    "$$ 0.05 = \\Phi\\left( \\frac{(x - \\mu)}{\\sigma}\\right) $$ \n",
    "\n",
    "$\\Phi$ being the [cumulative distribution for the normal distribution](http://en.wikipedia.org/wiki/Normal_distribution#Cumulative_distribution)\n",
    "\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Exercises\n",
    "\n",
    "1\\. How would you estimate the quantity $E\\left[ \\cos{X} \\right]$, where $X \\sim \\text{Exp}(4)$? What about $E\\left[ \\cos{X} | X \\lt 1\\right]$, i.e. the expected value *given* we know $X$ is less than 1? Would you need more samples than the original samples size to be equally accurate?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false,
    "jupyter": {
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    "## Enter code here\n",
    "import scipy.stats as stats\n",
    "exp = stats.expon( scale=4 )\n",
    "N = 1e5\n",
    "X = exp.rvs( int(N) )\n",
    "## ..."
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    "2\\. The following table was located in the paper \"Going for Three: Predicting the Likelihood of Field Goal Success with Logistic Regression\" [2]. The table ranks football field-goal kickers by their percent of non-misses. What mistake have the researchers made?\n",
    "\n",
    "-----\n",
    "\n",
    "####  Kicker Careers Ranked by Make Percentage\n",
    "<table><tbody><tr><th>Rank </th><th>Kicker </th><th>Make % </th><th>Number  of Kicks</th></tr><tr><td>1 </td><td>Garrett Hartley </td><td>87.7 </td><td>57</td></tr><tr><td>2</td><td> Matt Stover </td><td>86.8 </td><td>335</td></tr><tr><td>3 </td><td>Robbie Gould </td><td>86.2 </td><td>224</td></tr><tr><td>4 </td><td>Rob Bironas </td><td>86.1 </td><td>223</td></tr><tr><td>5</td><td> Shayne Graham </td><td>85.4 </td><td>254</td></tr><tr><td>… </td><td>… </td><td>…</td><td> </td></tr><tr><td>51</td><td> Dave Rayner </td><td>72.2 </td><td>90</td></tr><tr><td>52</td><td> Nick Novak </td><td>71.9 </td><td>64</td></tr><tr><td>53 </td><td>Tim Seder </td><td>71.0 </td><td>62</td></tr><tr><td>54 </td><td>Jose Cortez </td><td>70.7</td><td> 75</td></tr><tr><td>55 </td><td>Wade Richey </td><td>66.1</td><td> 56</td></tr></tbody></table>"
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    "In August 2013, [a popular post](http://bpodgursky.wordpress.com/2013/08/21/average-income-per-programming-language/) on the average income per programmer of different languages was trending. Here's the summary chart: (reproduced without permission, cause when you lie with stats, you gunna get the hammer). What do you notice about the extremes?\n",
    "\n",
    "------\n",
    "\n",
    "#### Average household income by programming language\n",
    "\n",
    "<table >\n",
    " <tr><td>Language</td><td>Average Household Income ($)</td><td>Data Points</td></tr>\n",
    " <tr><td>Puppet</td><td>87,589.29</td><td>112</td></tr>\n",
    " <tr><td>Haskell</td><td>89,973.82</td><td>191</td></tr>\n",
    " <tr><td>PHP</td><td>94,031.19</td><td>978</td></tr>\n",
    " <tr><td>CoffeeScript</td><td>94,890.80</td><td>435</td></tr>\n",
    " <tr><td>VimL</td><td>94,967.11</td><td>532</td></tr>\n",
    " <tr><td>Shell</td><td>96,930.54</td><td>979</td></tr>\n",
    " <tr><td>Lua</td><td>96,930.69</td><td>101</td></tr>\n",
    " <tr><td>Erlang</td><td>97,306.55</td><td>168</td></tr>\n",
    " <tr><td>Clojure</td><td>97,500.00</td><td>269</td></tr>\n",
    " <tr><td>Python</td><td>97,578.87</td><td>2314</td></tr>\n",
    " <tr><td>JavaScript</td><td>97,598.75</td><td>3443</td></tr>\n",
    " <tr><td>Emacs Lisp</td><td>97,774.65</td><td>355</td></tr>\n",
    " <tr><td>C#</td><td>97,823.31</td><td>665</td></tr>\n",
    " <tr><td>Ruby</td><td>98,238.74</td><td>3242</td></tr>\n",
    " <tr><td>C++</td><td>99,147.93</td><td>845</td></tr>\n",
    " <tr><td>CSS</td><td>99,881.40</td><td>527</td></tr>\n",
    " <tr><td>Perl</td><td>100,295.45</td><td>990</td></tr>\n",
    " <tr><td>C</td><td>100,766.51</td><td>2120</td></tr>\n",
    " <tr><td>Go</td><td>101,158.01</td><td>231</td></tr>\n",
    " <tr><td>Scala</td><td>101,460.91</td><td>243</td></tr>\n",
    " <tr><td>ColdFusion</td><td>101,536.70</td><td>109</td></tr>\n",
    " <tr><td>Objective-C</td><td>101,801.60</td><td>562</td></tr>\n",
    " <tr><td>Groovy</td><td>102,650.86</td><td>116</td></tr>\n",
    " <tr><td>Java</td><td>103,179.39</td><td>1402</td></tr>\n",
    " <tr><td>XSLT</td><td>106,199.19</td><td>123</td></tr>\n",
    " <tr><td>ActionScript</td><td>108,119.47</td><td>113</td></tr>\n",
    "</table>"
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    "### References\n",
    "\n",
    "1. Wainer, Howard. *The Most Dangerous Equation*. American Scientist, Volume 95.\n",
    "2. Clarck, Torin K., Aaron W. Johnson, and Alexander J. Stimpson. \"Going for Three: Predicting the Likelihood of Field Goal Success with Logistic Regression.\" (2013): n. page. [Web](http://www.sloansportsconference.com/wp-content/uploads/2013/Going%20for%20Three%20Predicting%20the%20Likelihood%20of%20Field%20Goal%20Success%20with%20Logistic%20Regression.pdf). 20 Feb. 2013.\n",
    "3. http://en.wikipedia.org/wiki/Beta_function#Incomplete_beta_function"
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